2024
Porras-Segovia, Alejandro; Granda-Beltrán, Ana Maria De; Gallardo, Claudia; Abascal-Peiró, Sofía; Barrigón, María Luisa; Artés-Rodríguez, Antonio; López-Castroman, Jorge; Courtet, Philippe; Baca-García, Enrique
Smartphone-based safety plan for suicidal crisis: The SmartCrisis 2.0 pilot study Artículo de revista
En: Journal of Psychiatric Research, vol. 169, pp. 284-291, 2024, ISSN: 0022-3956.
Resumen | Enlaces | BibTeX | Etiquetas: Ecological momentary intervention, Experience-sampling method, Suicide, Suicide attempt, Suicide ideation, Time-sampling procedures
@article{PORRASSEGOVIA2024284,
title = {Smartphone-based safety plan for suicidal crisis: The SmartCrisis 2.0 pilot study},
author = {Alejandro Porras-Segovia and Ana Maria De Granda-Beltr\'{a}n and Claudia Gallardo and Sof\'{i}a Abascal-Peir\'{o} and Mar\'{i}a Luisa Barrig\'{o}n and Antonio Art\'{e}s-Rodr\'{i}guez and Jorge L\'{o}pez-Castroman and Philippe Courtet and Enrique Baca-Garc\'{i}a},
url = {https://www.sciencedirect.com/science/article/pii/S0022395623005526},
doi = {https://doi.org/10.1016/j.jpsychires.2023.11.039},
issn = {0022-3956},
year = {2024},
date = {2024-01-01},
journal = {Journal of Psychiatric Research},
volume = {169},
pages = {284-291},
abstract = {Here we present the findings of the pilot phase of the SmartCrisis 2.0 Randomized Clinical Trial. This pilot study aimed to explore the feasibility and acceptability of a safety plan contained in a smartphone app. Our sample consisted patients with a history of recent suicidal behaviour who installed a smartphone-based safety plan. To explore the satisfaction with of the safety plan, two patient satisfaction surveys were conducted: one qualitative and one quantitative. To explore the objective use of the safety plan, we gained access to texts contained in the safety plans completed by the patients. Participation rate was 77%, while 48.9% patients completed both satisfaction surveys at the end of the pilot phase. N = 105 successfully installed the safety plan. In a scale from 1 to 10, users rated the usefulness of the security plan at 7.4, the usability at 8.9, the degree to which they would recommend it to others at 8.6 and the overall satisfaction with the project including evaluations at 9.6. The most widely completed tab was warning signs. Feeling sad or lonely was the warning sign most commonly reported by patients. The second most completed tab was internal coping strategies. Walking or practicing any other exercise was the strategy most commonly resorted to. Our smartphone-based safety plan appears to be a feasible intervention. Data obtained from this pilot study showed high participation rates and high acceptability by patients. This, together with the general satisfaction with the project, supports its implementation in the clinical practice.},
keywords = {Ecological momentary intervention, Experience-sampling method, Suicide, Suicide attempt, Suicide ideation, Time-sampling procedures},
pubstate = {published},
tppubtype = {article}
}
Here we present the findings of the pilot phase of the SmartCrisis 2.0 Randomized Clinical Trial. This pilot study aimed to explore the feasibility and acceptability of a safety plan contained in a smartphone app. Our sample consisted patients with a history of recent suicidal behaviour who installed a smartphone-based safety plan. To explore the satisfaction with of the safety plan, two patient satisfaction surveys were conducted: one qualitative and one quantitative. To explore the objective use of the safety plan, we gained access to texts contained in the safety plans completed by the patients. Participation rate was 77%, while 48.9% patients completed both satisfaction surveys at the end of the pilot phase. N = 105 successfully installed the safety plan. In a scale from 1 to 10, users rated the usefulness of the security plan at 7.4, the usability at 8.9, the degree to which they would recommend it to others at 8.6 and the overall satisfaction with the project including evaluations at 9.6. The most widely completed tab was warning signs. Feeling sad or lonely was the warning sign most commonly reported by patients. The second most completed tab was internal coping strategies. Walking or practicing any other exercise was the strategy most commonly resorted to. Our smartphone-based safety plan appears to be a feasible intervention. Data obtained from this pilot study showed high participation rates and high acceptability by patients. This, together with the general satisfaction with the project, supports its implementation in the clinical practice.
2023
Sükei, Emese; Romero-Medrano, Lorena; Leon-Martinez, Santiago; López, Jesús Herrera; Campaña-Montes, Juan José; Olmos, Pablo M; Baca-Garcia, Enrique; Artés-Rodríguez, Antonio
Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study Artículo de revista
En: JMIR Form Res, vol. 7, pp. e47167, 2023, ISSN: 2561-326X.
Resumen | Enlaces | BibTeX | Etiquetas: WHODAS; functional limitations; mobile sensing; passive ecological momentary assessment; predictive modeling; interpretable machine learning; machine learning; disability; clinical outcome
@article{info:doi/10.2196/47167,
title = {Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study},
author = {Emese S\"{u}kei and Lorena Romero-Medrano and Santiago Leon-Martinez and Jes\'{u}s Herrera L\'{o}pez and Juan Jos\'{e} Campa\~{n}a-Montes and Pablo M Olmos and Enrique Baca-Garcia and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.ncbi.nlm.nih.gov/pubmed/37902823},
doi = {10.2196/47167},
issn = {2561-326X},
year = {2023},
date = {2023-10-30},
journal = {JMIR Form Res},
volume = {7},
pages = {e47167},
abstract = {Background: Functional limitations are associated with poor clinical outcomes, higher mortality, and disability rates, especially in older adults. Continuous assessment of patients' functionality is important for clinical practice; however, traditional questionnaire-based assessment methods are very time-consuming and infrequently used. Mobile sensing offers a great range of sources that can assess function and disability daily. Objective: This work aims to prove the feasibility of an interpretable machine learning pipeline for predicting function and disability based on the World Health Organization Disability Assessment Schedule (WHODAS) 2.0 outcomes of clinical outpatients, using passively collected digital biomarkers. Methods: One-month-long behavioral time-series data consisting of physical and digital activity descriptor variables were summarized using statistical measures (minimum, maximum, mean, median, SD, and IQR), creating 64 features that were used for prediction. We then applied a sequential feature selection to each WHODAS 2.0 domain (cognition, mobility, self-care, getting along, life activities, and participation) in order to find the most descriptive features for each domain. Finally, we predicted the WHODAS 2.0 functional domain scores using linear regression using the best feature subsets. We reported the mean absolute errors and the mean absolute percentage errors over 4 folds as goodness-of-fit statistics to evaluate the model and allow for between-domain performance comparison. Results: Our machine learning\textendashbased models for predicting patients' WHODAS functionality scores per domain achieved an average (across the 6 domains) mean absolute percentage error of 19.5%, varying between 14.86% (self-care domain) and 27.21% (life activities domain). We found that 5-19 features were sufficient for each domain, and the most relevant being the distance traveled, time spent at home, time spent walking, exercise time, and vehicle time. Conclusions: Our findings show the feasibility of using machine learning\textendashbased methods to assess functional health solely from passively sensed mobile data. The feature selection step provides a set of interpretable features for each domain, ensuring better explainability to the models' decisions\textemdashan important aspect in clinical practice.},
keywords = {WHODAS; functional limitations; mobile sensing; passive ecological momentary assessment; predictive modeling; interpretable machine learning; machine learning; disability; clinical outcome},
pubstate = {published},
tppubtype = {article}
}
Background: Functional limitations are associated with poor clinical outcomes, higher mortality, and disability rates, especially in older adults. Continuous assessment of patients' functionality is important for clinical practice; however, traditional questionnaire-based assessment methods are very time-consuming and infrequently used. Mobile sensing offers a great range of sources that can assess function and disability daily. Objective: This work aims to prove the feasibility of an interpretable machine learning pipeline for predicting function and disability based on the World Health Organization Disability Assessment Schedule (WHODAS) 2.0 outcomes of clinical outpatients, using passively collected digital biomarkers. Methods: One-month-long behavioral time-series data consisting of physical and digital activity descriptor variables were summarized using statistical measures (minimum, maximum, mean, median, SD, and IQR), creating 64 features that were used for prediction. We then applied a sequential feature selection to each WHODAS 2.0 domain (cognition, mobility, self-care, getting along, life activities, and participation) in order to find the most descriptive features for each domain. Finally, we predicted the WHODAS 2.0 functional domain scores using linear regression using the best feature subsets. We reported the mean absolute errors and the mean absolute percentage errors over 4 folds as goodness-of-fit statistics to evaluate the model and allow for between-domain performance comparison. Results: Our machine learning–based models for predicting patients' WHODAS functionality scores per domain achieved an average (across the 6 domains) mean absolute percentage error of 19.5%, varying between 14.86% (self-care domain) and 27.21% (life activities domain). We found that 5-19 features were sufficient for each domain, and the most relevant being the distance traveled, time spent at home, time spent walking, exercise time, and vehicle time. Conclusions: Our findings show the feasibility of using machine learning–based methods to assess functional health solely from passively sensed mobile data. The feature selection step provides a set of interpretable features for each domain, ensuring better explainability to the models' decisions—an important aspect in clinical practice.
Barrigon, Maria Luisa; Romero-Medrano, Lorena; Moreno-Muñoz, Pablo; Porras-Segovia, Alejandro; Lopez-Castroman, Jorge; Courtet, Philippe; Artés-Rodríguez, Antonio; Baca-Garcia, Enrique
One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study Artículo de revista
En: J Med Internet Res, vol. 25, pp. e43719, 2023, ISSN: 1438-8871.
Resumen | Enlaces | BibTeX | Etiquetas: e-health; m-health; Ecological Mometary Asssessment; risk prediction; sensor monitoring; suicidal; suicide attempt; suicide
@article{info:doi/10.2196/43719,
title = {One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study},
author = {Maria Luisa Barrigon and Lorena Romero-Medrano and Pablo Moreno-Mu\~{n}oz and Alejandro Porras-Segovia and Jorge Lopez-Castroman and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garcia},
url = {http://www.ncbi.nlm.nih.gov/pubmed/37656498},
doi = {10.2196/43719},
issn = {1438-8871},
year = {2023},
date = {2023-05-01},
journal = {J Med Internet Res},
volume = {25},
pages = {e43719},
abstract = {Background: Suicide is a major global public health issue that is becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach. Objective: We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized through real-time smartphone monitoring in a cohort of patients with suicidal ideation. Methods: We recruited 225 patients between February 2018 and March 2020 with a history of suicidal thoughts and behavior as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on the risk of suicide or mental health crises. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide-risk events was tested. Results: During follow-up, 18 (8%) participants attempted suicide, and 14 (6.2%) presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of 1 week with an area under the curve of 0.78, indicating good accuracy. Conclusions: We describe an innovative method to identify mental health crises based on passively collected information from patients' smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crises.},
keywords = {e-health; m-health; Ecological Mometary Asssessment; risk prediction; sensor monitoring; suicidal; suicide attempt; suicide},
pubstate = {published},
tppubtype = {article}
}
Background: Suicide is a major global public health issue that is becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach. Objective: We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized through real-time smartphone monitoring in a cohort of patients with suicidal ideation. Methods: We recruited 225 patients between February 2018 and March 2020 with a history of suicidal thoughts and behavior as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on the risk of suicide or mental health crises. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide-risk events was tested. Results: During follow-up, 18 (8%) participants attempted suicide, and 14 (6.2%) presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of 1 week with an area under the curve of 0.78, indicating good accuracy. Conclusions: We describe an innovative method to identify mental health crises based on passively collected information from patients' smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crises.
Romero-Medrano, Lorena; Artés-Rodríguez, Antonio
Multi-Source Change-Point Detection over Local Observation Models Artículo de revista
En: Pattern Recognition, vol. 134, pp. 109116, 2023.
BibTeX | Etiquetas:
@article{romero2023multi,
title = {Multi-Source Change-Point Detection over Local Observation Models},
author = {Lorena Romero-Medrano and Antonio Art\'{e}s-Rodr\'{i}guez},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Pattern Recognition},
volume = {134},
pages = {109116},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Peis, Ignacio; Olmos, Pablo M; Artés-Rodríguez, Antonio
Unsupervised learning of global factors in deep generative models Artículo de revista
En: Pattern Recognition, vol. 134, pp. 109130, 2023.
BibTeX | Etiquetas:
@article{peis2023unsupervised,
title = {Unsupervised learning of global factors in deep generative models},
author = {Ignacio Peis and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Pattern Recognition},
volume = {134},
pages = {109130},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Moreno-Pino, Fernando; Olmos, Pablo M; Artés-Rodríguez, Antonio
Deep Autoregressive Models with Spectral Attention Artículo de revista
En: Pattern Recognition, pp. 109014, 2023, ISSN: 0031-3203.
Resumen | Enlaces | BibTeX | Etiquetas: Attention models, Deep learning, Filtering, global-local contexts, Signal processing, spectral domain attention, time series forecasting
@article{MORENOPINO2022109014,
title = {Deep Autoregressive Models with Spectral Attention},
author = {Fernando Moreno-Pino and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {https://www.sciencedirect.com/science/article/pii/S0031320322004940},
doi = {https://doi.org/10.1016/j.patcog.2022.109014},
issn = {0031-3203},
year = {2023},
date = {2023-01-01},
urldate = {2022-01-01},
journal = {Pattern Recognition},
pages = {109014},
abstract = {Time series forecasting is an important problem across many domains, playing a crucial role in multiple real-world applications. In this paper, we propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module, which merges global and local frequency domain information in the model’s embedded space. By characterizing in the spectral domain the embedding of the time series as occurrences of a random process, our method can identify global trends and seasonality patterns. Two spectral attention models, global and local to the time series, integrate this information within the forecast and perform spectral filtering to remove time series’s noise. The proposed architecture has a number of useful properties: it can be effectively incorporated into well-known forecast architectures, requiring a low number of parameters and producing explainable results that improve forecasting accuracy. We test the Spectral Attention Autoregressive Model (SAAM) on several well-known forecast datasets, consistently demonstrating that our model compares favorably to state-of-the-art approaches.},
keywords = {Attention models, Deep learning, Filtering, global-local contexts, Signal processing, spectral domain attention, time series forecasting},
pubstate = {published},
tppubtype = {article}
}
Time series forecasting is an important problem across many domains, playing a crucial role in multiple real-world applications. In this paper, we propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module, which merges global and local frequency domain information in the model’s embedded space. By characterizing in the spectral domain the embedding of the time series as occurrences of a random process, our method can identify global trends and seasonality patterns. Two spectral attention models, global and local to the time series, integrate this information within the forecast and perform spectral filtering to remove time series’s noise. The proposed architecture has a number of useful properties: it can be effectively incorporated into well-known forecast architectures, requiring a low number of parameters and producing explainable results that improve forecasting accuracy. We test the Spectral Attention Autoregressive Model (SAAM) on several well-known forecast datasets, consistently demonstrating that our model compares favorably to state-of-the-art approaches.
Sedano-Capdevila, Alba; Toledo-Acosta, Mauricio; Barrigon, María Luisa; Morales-González, Eliseo; Torres-Moreno, David; Martínez-Zaldivar, Bolívar; Hermosillo-Valadez, Jorge; Baca-García, Enrique; Aroca, Fuensanta; Artes-Rodriguez, Antonio; Baca-García, Enrique; Berrouiguet, Sofian; Billot, Romain; Carballo-Belloso, Juan Jose; Courtet, Philippe; Gomez, David Delgado; Lopez-Castroman, Jorge; Rodriguez, Mercedes Perez; Aznar-Carbone, Julia; Cegla, Fanny; Gutiérrez-Recacha, Pedro; Izaguirre-Gamir, Leire; Herrera-Sanchez, Javier; Borja, Marta Migoya; Palomar-Ciria, Nora; Martínez, Adela Sánchez-Escribano; Vasquez, Manuel; Vallejo-Oñate, Silvia; Vera-Varela, Constanza; Amodeo-Escribano, Susana; Arrua, Elsa; Bautista, Olga; Barrigón, Maria Luisa; Carmona, Rodrigo; Caro-Cañizares, Irene; Carollo-Vivian, Sonia; Chamorro, Jaime; González-Granado, Marta; Iza, Miren; Jiménez-Giménez, Mónica; López-Gómez, Ana; Mata-Iturralde, Laura; Miguelez, Carolina; Muñoz-Lorenzo, Laura; Navarro-Jiménez, Rocío; Ovejero, Santiago; Palacios, María Luz; Pérez-Fominaya, Margarita; Peñuelas-Calvo, Inmaculada; Pérez-Colmenero, Sonia; Rico-Romano, Ana; Rodriguez-Jover, Alba; SánchezAlonso, Sergio; Sevilla-Vicente, Juncal; Vigil-López, Carolina; Villoria-Borrego, Lucía; Martin-Calvo, Marisa; Alcón-Durán, Ana; Stasio, Ezequiel Di; García-Vega, Juan Manuel; Martín-Calvo, Pedro; Ortega, Ana José; Segura-Valverde, Marta; Bañón-González, Sara María; Crespo-Llanos, Edurne; Codesal-Julián, Rosana; Frade-Ciudad, Ainara; Merino, Elena Hernando; Álvarez-García, Raquel; Coll-Font, Jose Marcos; Antonio, Pablo Portillo-de; Puras-Rico, Pablo; Sedano-Capdevila, Alba; Serrano-Marugán, Leticia
Text mining methods for the characterisation of suicidal thoughts and behaviour Artículo de revista
En: Psychiatry Research, vol. 322, pp. 115090, 2023, ISSN: 0165-1781.
Resumen | Enlaces | BibTeX | Etiquetas: Machine learning, Mobile health, Natural language processing, Suicidal ideation, Suicide, Suicide attempt
@article{SEDANOCAPDEVILA2023115090,
title = {Text mining methods for the characterisation of suicidal thoughts and behaviour},
author = {Alba Sedano-Capdevila and Mauricio Toledo-Acosta and Mar\'{i}a Luisa Barrigon and Eliseo Morales-Gonz\'{a}lez and David Torres-Moreno and Bol\'{i}var Mart\'{i}nez-Zaldivar and Jorge Hermosillo-Valadez and Enrique Baca-Garc\'{i}a and Fuensanta Aroca and Antonio Artes-Rodriguez and Enrique Baca-Garc\'{i}a and Sofian Berrouiguet and Romain Billot and Juan Jose Carballo-Belloso and Philippe Courtet and David Delgado Gomez and Jorge Lopez-Castroman and Mercedes Perez Rodriguez and Julia Aznar-Carbone and Fanny Cegla and Pedro Guti\'{e}rrez-Recacha and Leire Izaguirre-Gamir and Javier Herrera-Sanchez and Marta Migoya Borja and Nora Palomar-Ciria and Adela S\'{a}nchez-Escribano Mart\'{i}nez and Manuel Vasquez and Silvia Vallejo-O\~{n}ate and Constanza Vera-Varela and Susana Amodeo-Escribano and Elsa Arrua and Olga Bautista and Maria Luisa Barrig\'{o}n and Rodrigo Carmona and Irene Caro-Ca\~{n}izares and Sonia Carollo-Vivian and Jaime Chamorro and Marta Gonz\'{a}lez-Granado and Miren Iza and M\'{o}nica Jim\'{e}nez-Gim\'{e}nez and Ana L\'{o}pez-G\'{o}mez and Laura Mata-Iturralde and Carolina Miguelez and Laura Mu\~{n}oz-Lorenzo and Roc\'{i}o Navarro-Jim\'{e}nez and Santiago Ovejero and Mar\'{i}a Luz Palacios and Margarita P\'{e}rez-Fominaya and Inmaculada Pe\~{n}uelas-Calvo and Sonia P\'{e}rez-Colmenero and Ana Rico-Romano and Alba Rodriguez-Jover and Sergio S\'{a}nchezAlonso and Juncal Sevilla-Vicente and Carolina Vigil-L\'{o}pez and Luc\'{i}a Villoria-Borrego and Marisa Martin-Calvo and Ana Alc\'{o}n-Dur\'{a}n and Ezequiel Di Stasio and Juan Manuel Garc\'{i}a-Vega and Pedro Mart\'{i}n-Calvo and Ana Jos\'{e} Ortega and Marta Segura-Valverde and Sara Mar\'{i}a Ba\~{n}\'{o}n-Gonz\'{a}lez and Edurne Crespo-Llanos and Rosana Codesal-Juli\'{a}n and Ainara Frade-Ciudad and Elena Hernando Merino and Raquel \'{A}lvarez-Garc\'{i}a and Jose Marcos Coll-Font and Pablo Portillo-de Antonio and Pablo Puras-Rico and Alba Sedano-Capdevila and Leticia Serrano-Marug\'{a}n},
url = {https://www.sciencedirect.com/science/article/pii/S0165178123000434},
doi = {https://doi.org/10.1016/j.psychres.2023.115090},
issn = {0165-1781},
year = {2023},
date = {2023-01-01},
journal = {Psychiatry Research},
volume = {322},
pages = {115090},
abstract = {Traditional research methods have shown low predictive value for suicidal risk assessments and limitations to be applied in clinical practice. The authors sought to evaluate natural language processing as a new tool for assessing self-injurious thoughts and behaviors and emotions related. We used MEmind project to assess 2838 psychiatric outpatients. Anonymous unstructured responses to the open-ended question “how are you feeling today?” were collected according to their emotional state. Natural language processing was used to process the patients' writings. The texts were automatically represented (corpus) and analyzed to determine their emotional content and degree of suicidal risk. Authors compared the patients' texts with a question used to assess lack of desire to live, as a suicidal risk assessment tool. Corpus consists of 5,489 short free-text documents containing 12,256 tokenized or unique words. The natural language processing showed an ROC-AUC score of 0.9638 when compared with the responses to lack of a desire to live question. Natural language processing shows encouraging results for classifying subjects according to their desire not to live as a measure of suicidal risk using patients’ free texts. It is also easily applicable to clinical practice and facilitates real-time communication with patients, allowing better intervention strategies to be designed.},
keywords = {Machine learning, Mobile health, Natural language processing, Suicidal ideation, Suicide, Suicide attempt},
pubstate = {published},
tppubtype = {article}
}
Traditional research methods have shown low predictive value for suicidal risk assessments and limitations to be applied in clinical practice. The authors sought to evaluate natural language processing as a new tool for assessing self-injurious thoughts and behaviors and emotions related. We used MEmind project to assess 2838 psychiatric outpatients. Anonymous unstructured responses to the open-ended question “how are you feeling today?” were collected according to their emotional state. Natural language processing was used to process the patients' writings. The texts were automatically represented (corpus) and analyzed to determine their emotional content and degree of suicidal risk. Authors compared the patients' texts with a question used to assess lack of desire to live, as a suicidal risk assessment tool. Corpus consists of 5,489 short free-text documents containing 12,256 tokenized or unique words. The natural language processing showed an ROC-AUC score of 0.9638 when compared with the responses to lack of a desire to live question. Natural language processing shows encouraging results for classifying subjects according to their desire not to live as a measure of suicidal risk using patients’ free texts. It is also easily applicable to clinical practice and facilitates real-time communication with patients, allowing better intervention strategies to be designed.
Bonilla-Escribano, Pablo; Ramírez, David; Baca-García, Enrique; Courtet, Philippe; Artés-Rodríguez, Antonio; López-Castromán, Jorge
Multidimensional variability in ecological assessments predicts two clusters of suicidal patients Artículo de revista
En: Scientific reports, vol. 13, no 1, pp. 3546, 2023.
BibTeX | Etiquetas:
@article{bonilla2023multidimensional,
title = {Multidimensional variability in ecological assessments predicts two clusters of suicidal patients},
author = {Pablo Bonilla-Escribano and David Ram\'{i}rez and Enrique Baca-Garc\'{i}a and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez and Jorge L\'{o}pez-Castrom\'{a}n},
year = {2023},
date = {2023-01-01},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {3546},
publisher = {Nature Publishing Group UK London},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Aguilera, Aurora Cobo; Olmos, Pablo M; Artés-Rodríguez, Antonio; Pérez-Cruz, Fernando
Regularizing transformers with deep probabilistic layers Artículo de revista
En: Neural Networks, 2023, ISSN: 0893-6080.
Resumen | Enlaces | BibTeX | Etiquetas: Deep learning, Missing data, Natural language processing, Regularization, Transformers, Variational auto-encoder
@article{AGUILERA2023,
title = {Regularizing transformers with deep probabilistic layers},
author = {Aurora Cobo Aguilera and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez and Fernando P\'{e}rez-Cruz},
url = {https://www.sciencedirect.com/science/article/pii/S0893608023000448},
doi = {https://doi.org/10.1016/j.neunet.2023.01.032},
issn = {0893-6080},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Neural Networks},
abstract = {Language models (LM) have grown non-stop in the last decade, from sequence-to-sequence architectures to attention-based Transformers. However, regularization is not deeply studied in those structures. In this work, we use a Gaussian Mixture Variational Autoencoder (GMVAE) as a regularizer layer. We study its advantages regarding the depth where it is placed and prove its effectiveness in several scenarios. Experimental result demonstrates that the inclusion of deep generative models within Transformer-based architectures such as BERT, RoBERTa, or XLM-R can bring more versatile models, able to generalize better and achieve improved imputation score in tasks such as SST-2 and TREC or even impute missing/noisy words with richer text.},
keywords = {Deep learning, Missing data, Natural language processing, Regularization, Transformers, Variational auto-encoder},
pubstate = {published},
tppubtype = {article}
}
Language models (LM) have grown non-stop in the last decade, from sequence-to-sequence architectures to attention-based Transformers. However, regularization is not deeply studied in those structures. In this work, we use a Gaussian Mixture Variational Autoencoder (GMVAE) as a regularizer layer. We study its advantages regarding the depth where it is placed and prove its effectiveness in several scenarios. Experimental result demonstrates that the inclusion of deep generative models within Transformer-based architectures such as BERT, RoBERTa, or XLM-R can bring more versatile models, able to generalize better and achieve improved imputation score in tasks such as SST-2 and TREC or even impute missing/noisy words with richer text.
Sükei, Emese; Leon-Martinez, Santiago; Olmos, Pablo M; Artés-Rodríguez, Antonio
Automatic patient functionality assessment from multimodal data using deep learning techniques – Development and feasibility evaluation Artículo de revista
En: Internet Interventions, vol. 33, pp. 100657, 2023, ISSN: 2214-7829.
Resumen | Enlaces | BibTeX | Etiquetas: Attention models, Digital phenotyping, Ecological momentary assessment, In-situ patient monitoring, Time-series modelling, Transfer learning
@article{SUKEI2023100657,
title = {Automatic patient functionality assessment from multimodal data using deep learning techniques \textendash Development and feasibility evaluation},
author = {Emese S\"{u}kei and Santiago Leon-Martinez and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {https://www.sciencedirect.com/science/article/pii/S221478292300057X},
doi = {https://doi.org/10.1016/j.invent.2023.100657},
issn = {2214-7829},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Internet Interventions},
volume = {33},
pages = {100657},
abstract = {Wearable devices and mobile sensors enable the real-time collection of an abundant source of physiological and behavioural data unobtrusively. Unlike traditional in-person evaluation or ecological momentary assessment (EMA) questionnaire-based approaches, these data sources open many possibilities in remote patient monitoring. However, defining robust models is challenging due to the data's noisy and frequently missing observations. This work proposes an attention-based Long Short-Term Memory (LSTM) neural network-based pipeline for predicting mobility impairment based on WHODAS 2.0 evaluation from such digital biomarkers. Furthermore, we addressed the missing observation problem by utilising hidden Markov models and the possibility of including information from unlabelled samples via transfer learning. We validated our approach using two wearable/mobile sensor data sets collected in the wild and socio-demographic information about the patients. Our results showed that in the WHODAS 2.0 mobility impairment prediction task, the proposed pipeline outperformed a prior baseline while additionally providing interpretability with attention heatmaps. Moreover, using a much smaller cohort via task transfer learning, the same model could learn to predict generalised anxiety severity accurately based on GAD-7 scores.},
keywords = {Attention models, Digital phenotyping, Ecological momentary assessment, In-situ patient monitoring, Time-series modelling, Transfer learning},
pubstate = {published},
tppubtype = {article}
}
Wearable devices and mobile sensors enable the real-time collection of an abundant source of physiological and behavioural data unobtrusively. Unlike traditional in-person evaluation or ecological momentary assessment (EMA) questionnaire-based approaches, these data sources open many possibilities in remote patient monitoring. However, defining robust models is challenging due to the data's noisy and frequently missing observations. This work proposes an attention-based Long Short-Term Memory (LSTM) neural network-based pipeline for predicting mobility impairment based on WHODAS 2.0 evaluation from such digital biomarkers. Furthermore, we addressed the missing observation problem by utilising hidden Markov models and the possibility of including information from unlabelled samples via transfer learning. We validated our approach using two wearable/mobile sensor data sets collected in the wild and socio-demographic information about the patients. Our results showed that in the WHODAS 2.0 mobility impairment prediction task, the proposed pipeline outperformed a prior baseline while additionally providing interpretability with attention heatmaps. Moreover, using a much smaller cohort via task transfer learning, the same model could learn to predict generalised anxiety severity accurately based on GAD-7 scores.
2022
Porras-Segovia, Alejandro; Moreno, Manon; Barrigón, María Luisa; Castroman, Jorge López; Courtet, Philippe; Berrouiguet, Sofian; Artés-Rodríguez, Antonio; Baca-García, Enrique
Six-month clinical and ecological momentary assessment follow-up of patients at high risk of suicide: a survival analysis Artículo de revista
En: The Journal of Clinical Psychiatry, vol. 84, no 1, pp. 44594, 2022.
BibTeX | Etiquetas:
@article{porras2022six,
title = {Six-month clinical and ecological momentary assessment follow-up of patients at high risk of suicide: a survival analysis},
author = {Alejandro Porras-Segovia and Manon Moreno and Mar\'{i}a Luisa Barrig\'{o}n and Jorge L\'{o}pez Castroman and Philippe Courtet and Sofian Berrouiguet and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {The Journal of Clinical Psychiatry},
volume = {84},
number = {1},
pages = {44594},
publisher = {Physicians Postgraduate Press, Inc.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Barrigon, Maria Luisa; Porras-Segovia, Alejandro; Courtet, Philippe; Lopez-Castroman, Jorge; Berrouiguet, Sofian; Pérez-Rodríguez, María-Mercedes; Artés-Rodríguez, Antonio; Baca-Garcia, Enrique
Smartphone-based Ecological Momentary Intervention for secondary prevention of suicidal thoughts and behaviour: protocol for the SmartCrisis V. 2.0 randomised clinical trial Artículo de revista
En: BMJ open, vol. 12, no 9, pp. e051807, 2022.
BibTeX | Etiquetas:
@article{barrigon2022smartphone,
title = {Smartphone-based Ecological Momentary Intervention for secondary prevention of suicidal thoughts and behaviour: protocol for the SmartCrisis V. 2.0 randomised clinical trial},
author = {Maria Luisa Barrigon and Alejandro Porras-Segovia and Philippe Courtet and Jorge Lopez-Castroman and Sofian Berrouiguet and Mar\'{i}a-Mercedes P\'{e}rez-Rodr\'{i}guez and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garcia},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {BMJ open},
volume = {12},
number = {9},
pages = {e051807},
publisher = {British Medical Journal Publishing Group},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Barrejón, Daniel; Olmos, Pablo M; Artés-Rodríguez, Antonio
Medical Data Wrangling With Sequential Variational Autoencoders Artículo de revista
En: IEEE Journal of Biomedical and Health Informatics, vol. 26, no 6, pp. 2737-2745, 2022.
@article{9594658b,
title = {Medical Data Wrangling With Sequential Variational Autoencoders},
author = {Daniel Barrej\'{o}n and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {10.1109/JBHI.2021.3123839},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
volume = {26},
number = {6},
pages = {2737-2745},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Escudero-Vilaplana, Vicente; Romero-Medrano, Lorena; Villanueva-Bueno, Cristina; de Diago, Marta Rodríguez; Yánez-Montesdeoca, Alberto; Collado-Borrell, Roberto; Campaña-Montes, Juan José; Marzal-Alfaro, Belén; Revuelta-Herrero, José Luis; Calles, Antonio; Galera, Mar; Álvarez, Rosa; Herranz, Ana; Sanjurjo, María; Artés-Rodríguez, Antonio
En: Frontiers in oncology, vol. 12, pp. 880430, 2022, ISSN: 2234-943X.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{PMID:35936756,
title = {Smartphone-Based Ecological Momentary Assessment for the Measurement of the Performance Status and Health-Related Quality of Life in Cancer Patients Under Systemic Anticancer Therapies: Development and Acceptability of a Mobile App},
author = {Vicente Escudero-Vilaplana and Lorena Romero-Medrano and Cristina Villanueva-Bueno and Marta Rodr\'{i}guez de Diago and Alberto Y\'{a}nez-Montesdeoca and Roberto Collado-Borrell and Juan Jos\'{e} Campa\~{n}a-Montes and Bel\'{e}n Marzal-Alfaro and Jos\'{e} Luis Revuelta-Herrero and Antonio Calles and Mar Galera and Rosa \'{A}lvarez and Ana Herranz and Mar\'{i}a Sanjurjo and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {https://europepmc.org/articles/PMC9351705},
doi = {10.3389/fonc.2022.880430},
issn = {2234-943X},
year = {2022},
date = {2022-01-01},
journal = {Frontiers in oncology},
volume = {12},
pages = {880430},
abstract = {\<h4\>Background\</h4\>We have defined a project to develop a mobile app that continually records smartphone parameters which may help define the Eastern Cooperative Oncology Group performance status (ECOG-PS) and the health-related quality of life (HRQoL), without interaction with patients or professionals. This project is divided into 3 phases. Here we describe phase 1. The objective of this phase was to develop the app and assess its usability concerning patient characteristics, acceptability, and satisfaction.\<h4\>Methods\</h4\>The app eB2-ECOG was developed and installed in the smartphone of cancer patients who will be followed for six months. Criteria inclusion were: age over 18-year-old; diagnosed with unresectable or metastatic lung cancer, gastrointestinal stromal tumor, sarcoma, or head and neck cancer; under systemic anticancer therapies; and possession of a Smartphone. The app will collect passive and active data from the patients while healthcare professionals will evaluate the ECOG-PS and HRQoL through conventional tools. Acceptability was assessed during the follow-up. Patients answered a satisfaction survey in the app between 3-6 months from their inclusion.\<h4\>Results\</h4\>The app developed provides a system for continuously collecting, merging, and processing data related to patient's health and physical activity. It provides a transparent capture service based on all the available data of a patient. Currently, 106 patients have been recruited. A total of 36 patients were excluded, most of them (21/36) due to technological reasons. We assessed 69 patients (53 lung cancer, 8 gastrointestinal stromal tumors, 5 sarcomas, and 3 head and neck cancer). Concerning app satisfaction, 70.4% (20/27) of patients found the app intuitive and easy to use, and 51.9% (17/27) of them said that the app helped them to improve and handle their problems better. Overall, 17 out of 27 patients [62.9%] were satisfied with the app, and 14 of them [51.8%] would recommend the app to other patients.\<h4\>Conclusions\</h4\>We observed that the app's acceptability and satisfaction were good, which is essential for the continuity of the project. In the subsequent phases, we will develop predictive models based on the collected information during this phase. We will validate the method and analyze the sensitivity of the automated results.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
<h4>Background</h4>We have defined a project to develop a mobile app that continually records smartphone parameters which may help define the Eastern Cooperative Oncology Group performance status (ECOG-PS) and the health-related quality of life (HRQoL), without interaction with patients or professionals. This project is divided into 3 phases. Here we describe phase 1. The objective of this phase was to develop the app and assess its usability concerning patient characteristics, acceptability, and satisfaction.<h4>Methods</h4>The app eB2-ECOG was developed and installed in the smartphone of cancer patients who will be followed for six months. Criteria inclusion were: age over 18-year-old; diagnosed with unresectable or metastatic lung cancer, gastrointestinal stromal tumor, sarcoma, or head and neck cancer; under systemic anticancer therapies; and possession of a Smartphone. The app will collect passive and active data from the patients while healthcare professionals will evaluate the ECOG-PS and HRQoL through conventional tools. Acceptability was assessed during the follow-up. Patients answered a satisfaction survey in the app between 3-6 months from their inclusion.<h4>Results</h4>The app developed provides a system for continuously collecting, merging, and processing data related to patient's health and physical activity. It provides a transparent capture service based on all the available data of a patient. Currently, 106 patients have been recruited. A total of 36 patients were excluded, most of them (21/36) due to technological reasons. We assessed 69 patients (53 lung cancer, 8 gastrointestinal stromal tumors, 5 sarcomas, and 3 head and neck cancer). Concerning app satisfaction, 70.4% (20/27) of patients found the app intuitive and easy to use, and 51.9% (17/27) of them said that the app helped them to improve and handle their problems better. Overall, 17 out of 27 patients [62.9%] were satisfied with the app, and 14 of them [51.8%] would recommend the app to other patients.<h4>Conclusions</h4>We observed that the app's acceptability and satisfaction were good, which is essential for the continuity of the project. In the subsequent phases, we will develop predictive models based on the collected information during this phase. We will validate the method and analyze the sensitivity of the automated results.
de León, Santiago; Ruiz, Marta; Parra-Vargas, Elena; Chicchi-Giglioli, Irene; Courtet, Philippe; López-Castromán, Jorge; Artés-Rodríguez, Antonio; Baca-Garcia, Enrique; Porras-Segovia, Alejandro; Barrigon, Maria Luisa
En: BMJ Open, vol. 12, no 7, 2022, ISSN: 2044-6055.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{deLeon-Martineze058486,
title = {Virtual reality and speech analysis for the assessment of impulsivity and decision-making: protocol for a comparison with neuropsychological tasks and self-administered questionnaires},
author = {Santiago de Le\'{o}n and Marta Ruiz and Elena Parra-Vargas and Irene Chicchi-Giglioli and Philippe Courtet and Jorge L\'{o}pez-Castrom\'{a}n and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garcia and Alejandro Porras-Segovia and Maria Luisa Barrigon},
url = {https://bmjopen.bmj.com/content/12/7/e058486},
doi = {10.1136/bmjopen-2021-058486},
issn = {2044-6055},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {BMJ Open},
volume = {12},
number = {7},
publisher = {British Medical Journal Publishing Group},
abstract = {Introduction Impulsivity is present in a range of mental disorders and has been associated with suicide. Traditional measures of impulsivity have certain limitations, such as the lack of ecological validity. Virtual reality (VR) may overcome these issues. This study aims to validate the VR assessment tool ‘Spheres \& Shield Maze Task’ and speech analysis by comparing them with traditional measures. We hypothesise that these innovative tools will be reliable and acceptable by patients, potentially improving the simultaneous assessment of impulsivity and decision-making.Methods and analysis This study will be carried out at the University Hospital Fundaci\'{o}n Jim\'{e}nez D'iaz (Madrid, Spain). Our sample will consist of adults divided into three groups: psychiatric outpatients with a history of suicidal thoughts and/or behaviours, psychiatric outpatients without such a history and healthy volunteers. The target sample size was established at 300 participants (100 per group). Participants will complete the Barratt Impulsiveness Scale 11; the Urgency, Premeditation, Perseverance, Sensation Seeking, Positive Urgency, Impulsive Behaviour Scale; Iowa Gambling Task; Continuous Performance Test; Stop signal Task, and Go/no-go task, three questions of emotional affect, the Spheres \& Shield Maze Task and two satisfaction surveys. During these tasks, participant speech will be recorded. Construct validity of the VR environment will be calculated. We will also explore the association between VR-assessed impulsivity and history of suicidal thoughts and/or behaviour, and the association between speech and impulsivity and decision-making.Ethics and dissemination This study was approved by the Ethics Committee of the University Hospital Fundaci\'{o}n Jim\'{e}nez D'iaz (PIC128-21_FJD). Participants will be required to provide written informed consent. The findings will be presented in a series of manuscripts that will be submitted to peer-reviewed journals for publication.Trial registration number NCT05109845; Pre-results.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Introduction Impulsivity is present in a range of mental disorders and has been associated with suicide. Traditional measures of impulsivity have certain limitations, such as the lack of ecological validity. Virtual reality (VR) may overcome these issues. This study aims to validate the VR assessment tool ‘Spheres & Shield Maze Task’ and speech analysis by comparing them with traditional measures. We hypothesise that these innovative tools will be reliable and acceptable by patients, potentially improving the simultaneous assessment of impulsivity and decision-making.Methods and analysis This study will be carried out at the University Hospital Fundación Jiménez D'iaz (Madrid, Spain). Our sample will consist of adults divided into three groups: psychiatric outpatients with a history of suicidal thoughts and/or behaviours, psychiatric outpatients without such a history and healthy volunteers. The target sample size was established at 300 participants (100 per group). Participants will complete the Barratt Impulsiveness Scale 11; the Urgency, Premeditation, Perseverance, Sensation Seeking, Positive Urgency, Impulsive Behaviour Scale; Iowa Gambling Task; Continuous Performance Test; Stop signal Task, and Go/no-go task, three questions of emotional affect, the Spheres & Shield Maze Task and two satisfaction surveys. During these tasks, participant speech will be recorded. Construct validity of the VR environment will be calculated. We will also explore the association between VR-assessed impulsivity and history of suicidal thoughts and/or behaviour, and the association between speech and impulsivity and decision-making.Ethics and dissemination This study was approved by the Ethics Committee of the University Hospital Fundación Jiménez D'iaz (PIC128-21_FJD). Participants will be required to provide written informed consent. The findings will be presented in a series of manuscripts that will be submitted to peer-reviewed journals for publication.Trial registration number NCT05109845; Pre-results.
Romero-Medrano, Lorena; Moreno-Muñoz, P; Artés-Rodríguez, Antonio
Multinomial Sampling of Latent Variables for Hierarchical Change-Point Detection Artículo de revista
En: Journal of Signal Processing Systems, vol. 94, no 2, pp. 215–227, 2022.
BibTeX | Etiquetas:
@article{romero2022multinomial,
title = {Multinomial Sampling of Latent Variables for Hierarchical Change-Point Detection},
author = {Lorena Romero-Medrano and P Moreno-Mu\~{n}oz and Antonio Art\'{e}s-Rodr\'{i}guez},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Journal of Signal Processing Systems},
volume = {94},
number = {2},
pages = {215--227},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Porras-Segovia, Alejandro; Díaz-Oliván, Isaac; Barrigón, Maria Luisa; Moreno, Manon; Artés-Rodríguez, Antonio; Perez-Rodriguez, Mercedes M; Baca-García, Enrique
Real-world feasibility and acceptability of real-time suicide risk monitoring via smartphones: A 6-month follow-up cohort Artículo de revista
En: Journal of Psychiatric Research, vol. 149, pp. 145-154, 2022, ISSN: 0022-3956.
Resumen | Enlaces | BibTeX | Etiquetas: Ecological momentary assessment, eHealth, Mhealth, Suicide, Suicide attempt, Suicide ideation
@article{PORRASSEGOVIA2022145,
title = {Real-world feasibility and acceptability of real-time suicide risk monitoring via smartphones: A 6-month follow-up cohort},
author = {Alejandro Porras-Segovia and Isaac D\'{i}az-Oliv\'{a}n and Maria Luisa Barrig\'{o}n and Manon Moreno and Antonio Art\'{e}s-Rodr\'{i}guez and Mercedes M Perez-Rodriguez and Enrique Baca-Garc\'{i}a},
url = {https://www.sciencedirect.com/science/article/pii/S0022395622001078},
doi = {https://doi.org/10.1016/j.jpsychires.2022.02.026},
issn = {0022-3956},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Journal of Psychiatric Research},
volume = {149},
pages = {145-154},
abstract = {Active and passive Ecological Momentary Assessment of suicide risk is crucial for suicide prevention. We aimed to assess the feasibility and acceptability of active and passive smartphone-based EMA in real-world conditions in patients at high risk for suicide. We followed 393 patients at high risk for suicide for six months using two mobile health applications: the MEmind (active) and the eB2 (passive). Retention with active EMA was 79.3% after 1 month and 22.6% after 6 months. Retention with passive EMA was 87.8% after 1 month and 46.6% after 6 months. Satisfaction with the MEmind app, uninstalling the eB2 app and diagnosis of eating disorders were independently associated with stopping active EMA. Satisfaction with the eB2 app and uninstalling the MEmind app were independently associated with stopping passive EMA. Smartphone-based active and passive EMA are feasible and may increase accessibility to mental healthcare.},
keywords = {Ecological momentary assessment, eHealth, Mhealth, Suicide, Suicide attempt, Suicide ideation},
pubstate = {published},
tppubtype = {article}
}
Active and passive Ecological Momentary Assessment of suicide risk is crucial for suicide prevention. We aimed to assess the feasibility and acceptability of active and passive smartphone-based EMA in real-world conditions in patients at high risk for suicide. We followed 393 patients at high risk for suicide for six months using two mobile health applications: the MEmind (active) and the eB2 (passive). Retention with active EMA was 79.3% after 1 month and 22.6% after 6 months. Retention with passive EMA was 87.8% after 1 month and 46.6% after 6 months. Satisfaction with the MEmind app, uninstalling the eB2 app and diagnosis of eating disorders were independently associated with stopping active EMA. Satisfaction with the eB2 app and uninstalling the MEmind app were independently associated with stopping passive EMA. Smartphone-based active and passive EMA are feasible and may increase accessibility to mental healthcare.
2021
Ryu, J.; Sükei, Emese; Norbury, Agnes; H. Liu, S.; Campaña-Montes, Juan José; Baca-García, Enrique; Artés-Rodríguez, Antonio; Perez-Rodriguez, Mercedes M
Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning--Based Ecological Momentary Assessment Study Artículo de revista
En: JMIR Ment Health, vol. 8, no 9, pp. e30833, 2021, ISSN: 2368-7959.
Resumen | Enlaces | BibTeX | Etiquetas: änxiety disorder; COVID-19; social media; public health; digital phenotype; ecological momentary assessment; smartphone; machine learning; hidden Markov model"
@article{info:doi/10.2196/30833,
title = {Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning--Based Ecological Momentary Assessment Study},
author = {J. Ryu and Emese S\"{u}kei and Agnes Norbury and H. Liu, S. and Juan Jos\'{e} Campa\~{n}a-Montes and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez and Mercedes M Perez-Rodriguez},
url = {http://www.ncbi.nlm.nih.gov/pubmed/34524091},
doi = {10.2196/30833},
issn = {2368-7959},
year = {2021},
date = {2021-09-15},
urldate = {2021-09-15},
journal = {JMIR Ment Health},
volume = {8},
number = {9},
pages = {e30833},
abstract = {Background: Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored. Objective: We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. Methods: The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning--based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. Results: Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F},
keywords = {\"{a}nxiety disorder; COVID-19; social media; public health; digital phenotype; ecological momentary assessment; smartphone; machine learning; hidden Markov model\"},
pubstate = {published},
tppubtype = {article}
}
Background: Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored. Objective: We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. Methods: The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning--based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. Results: Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F
Lopez-Morinigo, Javier-David; Barrigón, María Luisa; Porras-Segovia, Alejandro; Ruiz-Ruano, Verónica González; Martínez, Adela Sánchez Escribano; Escobedo-Aedo, P. -J.; Sánchez-Alonso, S.; Mata-Iturralde, L.; Lorenzo, Laura Muñoz; Artés-Rodríguez, Antonio; David, Anthony S; Baca-García, Enrique
Use of Ecological Momentary Assessment Through a Passive Smartphone-Based App (eB2) by Patients With Schizophrenia: Acceptability Study Artículo de revista
En: J Med Internet Res, vol. 23, no 7, pp. e26548, 2021, ISSN: 1438-8871.
Resumen | Enlaces | BibTeX | Etiquetas: ecological momentary assessment; acceptability; schizophrenia spectrum disorders; eB2; digital tools; mental health; schizophrenia; real-time data; patients; digital health; internet; mobile apps
@article{info:doi/10.2196/26548,
title = {Use of Ecological Momentary Assessment Through a Passive Smartphone-Based App (eB2) by Patients With Schizophrenia: Acceptability Study},
author = {Javier-David Lopez-Morinigo and Mar\'{i}a Luisa Barrig\'{o}n and Alejandro Porras-Segovia and Ver\'{o}nica Gonz\'{a}lez Ruiz-Ruano and Adela S\'{a}nchez Escribano Mart\'{i}nez and P. -J. Escobedo-Aedo and S. S\'{a}nchez-Alonso and L. Mata-Iturralde and Laura Mu\~{n}oz Lorenzo and Antonio Art\'{e}s-Rodr\'{i}guez and Anthony S David and Enrique Baca-Garc\'{i}a},
url = {http://www.ncbi.nlm.nih.gov/pubmed/34309576},
doi = {10.2196/26548},
issn = {1438-8871},
year = {2021},
date = {2021-07-26},
urldate = {2021-07-26},
journal = {J Med Internet Res},
volume = {23},
number = {7},
pages = {e26548},
abstract = {Background: Ecological momentary assessment (EMA) tools appear to be useful interventions for collecting real-time data on patients' behavior and functioning. However, concerns have been voiced regarding the acceptability of EMA among patients with schizophrenia and the factors influencing EMA acceptability. Objective: The aim of this study was to investigate the acceptability of a passive smartphone-based EMA app, evidence-based behavior (eB2), among patients with schizophrenia spectrum disorders and the putative variables underlying their acceptance. Methods: The participants in this study were from an ongoing randomized controlled trial (RCT) of metacognitive training, consisting of outpatients with schizophrenia spectrum disorders (F20-29 of 10th revision of the International Statistical Classification of Diseases and Related Health Problems), aged 18-64 years, none of whom received any financial compensation. Those who consented to installation of the eB2 app (users) were compared with those who did not (nonusers) in sociodemographic, clinical, premorbid adjustment, neurocognitive, psychopathological, insight, and metacognitive variables. A multivariable binary logistic regression tested the influence of the above (independent) variables on ``being user versus nonuser'' (acceptability), which was the main outcome measure. Results: Out of the 77 RCT participants, 24 (31%) consented to installing eB2, which remained installed till the end of the study (median follow-up 14.50 weeks) in 14 participants (70%). Users were younger and had a higher education level, better premorbid adjustment, better executive function (according to the Trail Making Test), and higher cognitive insight levels (measured with the Beck Cognitive Insight Scale) than nonusers (univariate analyses) although only age (OR 0.93, 95% CI 0.86-0.99; P=.048) and early adolescence premorbid adjustment (OR 0.75, 95% CI 0.61-0.93; P=.01) survived the multivariable regression model, thus predicting eB2 acceptability. Conclusions: Acceptability of a passive smartphone-based EMA app among participants with schizophrenia spectrum disorders in this RCT where no participant received financial compensation was, as expected, relatively low, and linked with being young and good premorbid adjustment. Further research should examine how to increase EMA acceptability in patients with schizophrenia spectrum disorders, in particular, older participants and those with poor premorbid adjustment. Trial Registration: ClinicalTrials.gov NCT04104347; https://clinicaltrials.gov/ct2/show/NCT04104347},
keywords = {ecological momentary assessment; acceptability; schizophrenia spectrum disorders; eB2; digital tools; mental health; schizophrenia; real-time data; patients; digital health; internet; mobile apps},
pubstate = {published},
tppubtype = {article}
}
Background: Ecological momentary assessment (EMA) tools appear to be useful interventions for collecting real-time data on patients' behavior and functioning. However, concerns have been voiced regarding the acceptability of EMA among patients with schizophrenia and the factors influencing EMA acceptability. Objective: The aim of this study was to investigate the acceptability of a passive smartphone-based EMA app, evidence-based behavior (eB2), among patients with schizophrenia spectrum disorders and the putative variables underlying their acceptance. Methods: The participants in this study were from an ongoing randomized controlled trial (RCT) of metacognitive training, consisting of outpatients with schizophrenia spectrum disorders (F20-29 of 10th revision of the International Statistical Classification of Diseases and Related Health Problems), aged 18-64 years, none of whom received any financial compensation. Those who consented to installation of the eB2 app (users) were compared with those who did not (nonusers) in sociodemographic, clinical, premorbid adjustment, neurocognitive, psychopathological, insight, and metacognitive variables. A multivariable binary logistic regression tested the influence of the above (independent) variables on ``being user versus nonuser'' (acceptability), which was the main outcome measure. Results: Out of the 77 RCT participants, 24 (31%) consented to installing eB2, which remained installed till the end of the study (median follow-up 14.50 weeks) in 14 participants (70%). Users were younger and had a higher education level, better premorbid adjustment, better executive function (according to the Trail Making Test), and higher cognitive insight levels (measured with the Beck Cognitive Insight Scale) than nonusers (univariate analyses) although only age (OR 0.93, 95% CI 0.86-0.99; P=.048) and early adolescence premorbid adjustment (OR 0.75, 95% CI 0.61-0.93; P=.01) survived the multivariable regression model, thus predicting eB2 acceptability. Conclusions: Acceptability of a passive smartphone-based EMA app among participants with schizophrenia spectrum disorders in this RCT where no participant received financial compensation was, as expected, relatively low, and linked with being young and good premorbid adjustment. Further research should examine how to increase EMA acceptability in patients with schizophrenia spectrum disorders, in particular, older participants and those with poor premorbid adjustment. Trial Registration: ClinicalTrials.gov NCT04104347; https://clinicaltrials.gov/ct2/show/NCT04104347
Sükei, Emese; Norbury, Agnes; Perez-Rodriguez, Mercedes M; Olmos, Pablo M; Artés-Rodríguez, Antonio
Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach Artículo de revista
En: JMIR Mhealth Uhealth, vol. 9, no 3, pp. e24465, 2021, ISSN: 2291-5222.
Resumen | Enlaces | BibTeX | Etiquetas: mental health; affect; mobile health; mobile phone; digital phenotype; machine learning; Bayesian analysis; probabilistic models; personalized models
@article{info:doi/10.2196/24465,
title = {Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach},
author = {Emese S\"{u}kei and Agnes Norbury and Mercedes M Perez-Rodriguez and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.ncbi.nlm.nih.gov/pubmed/33749612},
doi = {10.2196/24465},
issn = {2291-5222},
year = {2021},
date = {2021-03-22},
journal = {JMIR Mhealth Uhealth},
volume = {9},
number = {3},
pages = {e24465},
abstract = {Background: Mental health disorders affect multiple aspects of patients' lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient's mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues. Objective: This study aims to present a machine learning--based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations. Methods: Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days' worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient. Results: Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals' overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days' data. Conclusions: These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients' mood states.},
keywords = {mental health; affect; mobile health; mobile phone; digital phenotype; machine learning; Bayesian analysis; probabilistic models; personalized models},
pubstate = {published},
tppubtype = {article}
}
Background: Mental health disorders affect multiple aspects of patients' lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient's mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues. Objective: This study aims to present a machine learning--based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations. Methods: Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days' worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient. Results: Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals' overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days' data. Conclusions: These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients' mood states.
Lopez-Castroman, Jorge; Abad-Tortosa, Diana; Aguilera, Aurora Cobo; Courtet, Philippe; Barrigón, Maria Luisa; Artés-Rodríguez, Antonio; Baca-García, Enrique
Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study Artículo de revista
En: JMIR Ment Health, vol. 8, no 1, pp. e17116, 2021, ISSN: 2368-7959.
Resumen | Enlaces | BibTeX | Etiquetas: mental disorders; suicide prevention; suicidal ideation; data mining; digital phenotyping
@article{info:doi/10.2196/17116,
title = {Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study},
author = {Jorge Lopez-Castroman and Diana Abad-Tortosa and Aurora Cobo Aguilera and Philippe Courtet and Maria Luisa Barrig\'{o}n and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
url = {http://www.ncbi.nlm.nih.gov/pubmed/33470943},
doi = {10.2196/17116},
issn = {2368-7959},
year = {2021},
date = {2021-01-20},
journal = {JMIR Ment Health},
volume = {8},
number = {1},
pages = {e17116},
abstract = {Background: New technologies are changing access to medical records and the relationship between physicians and patients. Professionals can now use e-mental health tools to provide prompt and personalized responses to patients with mental illness. However, there is a lack of knowledge about the digital phenotypes of patients who use e-mental health apps. Objective: This study aimed to reveal the profiles of users of a mental health app through machine learning techniques. Methods: We applied a nonparametric model, the Sparse Poisson Factorization Model, to discover latent features in the response patterns of 2254 psychiatric outpatients to a short self-assessment on general health. The assessment was completed through a mental health app after the first login. Results: The results showed the following four different profiles of patients: (1) all patients had feelings of worthlessness, aggressiveness, and suicidal ideas; (2) one in four reported low energy and difficulties to cope with problems; (3) less than a quarter described depressive symptoms with extremely high scores in suicidal thoughts and aggressiveness; and (4) a small number, possibly with the most severe conditions, reported a combination of all these features. Conclusions: User profiles did not overlap with clinician-made diagnoses. Since each profile seems to be associated with a different level of severity, the profiles could be useful for the prediction of behavioral risks among users of e-mental health apps.},
keywords = {mental disorders; suicide prevention; suicidal ideation; data mining; digital phenotyping},
pubstate = {published},
tppubtype = {article}
}
Background: New technologies are changing access to medical records and the relationship between physicians and patients. Professionals can now use e-mental health tools to provide prompt and personalized responses to patients with mental illness. However, there is a lack of knowledge about the digital phenotypes of patients who use e-mental health apps. Objective: This study aimed to reveal the profiles of users of a mental health app through machine learning techniques. Methods: We applied a nonparametric model, the Sparse Poisson Factorization Model, to discover latent features in the response patterns of 2254 psychiatric outpatients to a short self-assessment on general health. The assessment was completed through a mental health app after the first login. Results: The results showed the following four different profiles of patients: (1) all patients had feelings of worthlessness, aggressiveness, and suicidal ideas; (2) one in four reported low energy and difficulties to cope with problems; (3) less than a quarter described depressive symptoms with extremely high scores in suicidal thoughts and aggressiveness; and (4) a small number, possibly with the most severe conditions, reported a combination of all these features. Conclusions: User profiles did not overlap with clinician-made diagnoses. Since each profile seems to be associated with a different level of severity, the profiles could be useful for the prediction of behavioral risks among users of e-mental health apps.
Cobo, Aurora; Porras-Segovia, Alejandro; Perez-Rodriguez, Mercedes M; Artés-Rodríguez, Antonio; Barrigón, Maria Luisa; Courtet, Philippe; Baca-García, Enrique
Patients at high risk of suicide before and during a COVID-19 lockdown: ecological momentary assessment study Artículo de revista
En: BJPsych Open, vol. 7, no 3, pp. e82, 2021.
@article{cobo_porras-segovia_p\'{e}rez-rodr\'{i}guez_art\'{e}s-rodr\'{i}guez_barrig\'{o}n_courtet_baca-garc\'{i}a_2021,
title = {Patients at high risk of suicide before and during a COVID-19 lockdown: ecological momentary assessment study},
author = {Aurora Cobo and Alejandro Porras-Segovia and Mercedes M Perez-Rodriguez and Antonio Art\'{e}s-Rodr\'{i}guez and Maria Luisa Barrig\'{o}n and Philippe Courtet and Enrique Baca-Garc\'{i}a},
doi = {10.1192/bjo.2021.43},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {BJPsych Open},
volume = {7},
number = {3},
pages = {e82},
publisher = {Cambridge University Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lopez-Morinigo, Javier-David; Luisa, B. -E. Maria; Porras-Segovia, Alejandro; Martínez, Adela Sánchez Escribano; Escobedo-Aedo, P. -J.; Ruiz-Ruano, Verónica González; Mata-Iturralde, L.; Muñoz-Lorenzo, L.; Sánchez-Alonso, S.; Artés-Rodríguez, Antonio
En: European Psychiatry, vol. 64, no S1, pp. S343–S343, 2021.
@article{lopez-morinigo_luisa_porra_art\'{e}s-rodr\'{i}guez_etal._2021,
title = {Pending challenges to e-mental health in the COVID-19 era: Acceptability of a smartphone-based ecological momentary assessment application among patients with schizophrenia spectrum disorders},
author = {Javier-David Lopez-Morinigo and B. -E. Maria Luisa and Alejandro Porras-Segovia and Adela S\'{a}nchez Escribano Mart\'{i}nez and P. -J. Escobedo-Aedo and Ver\'{o}nica Gonz\'{a}lez Ruiz-Ruano and L. Mata-Iturralde and L. Mu\~{n}oz-Lorenzo and S. S\'{a}nchez-Alonso and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {10.1192/j.eurpsy.2021.920},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {European Psychiatry},
volume = {64},
number = {S1},
pages = {S343\textendashS343},
publisher = {Cambridge University Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Barrejon, Daniel; Olmos, Pablo M; Artes-Rodríguez, Antonio
Medical data wrangling with sequential variational autoencoders Artículo de revista
En: IEEE Journal of Biomedical and Health Informatics, pp. 1-1, 2021.
@article{9594658,
title = {Medical data wrangling with sequential variational autoencoders},
author = {Daniel Barrejon and Pablo M Olmos and Antonio Artes-Rodr\'{i}guez},
doi = {10.1109/JBHI.2021.3123839},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Moreno-Muñoz, P; Artés-Rodríguez, Antonio; Alvarez, Mauricio
Modular Gaussian Processes for Transfer Learning Artículo de revista
En: Advances in Neural Information Processing Systems, vol. 34, 2021.
BibTeX | Etiquetas:
@article{moreno2021modular,
title = {Modular Gaussian Processes for Transfer Learning},
author = {P Moreno-Mu\~{n}oz and Antonio Art\'{e}s-Rodr\'{i}guez and Mauricio Alvarez},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Advances in Neural Information Processing Systems},
volume = {34},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bonilla-Escribano, P; Ramírez, David; Porras-Segovia, Alejandro; Artés-Rodríguez, Antonio
Assessment of variability in irregularly sampled time series: Applications to mental healthcare Artículo de revista
En: Mathematics (Special issue on Recent Advances in Đata Science), vol. 9, no 1, 2021, ISSN: 2227-7390.
@article{Bonilla-EscribanoRamirezPorras-Segovia-2021,
title = {Assessment of variability in irregularly sampled time series: Applications to mental healthcare},
author = {P Bonilla-Escribano and David Ram\'{i}rez and Alejandro Porras-Segovia and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {10.3390/math9010071},
issn = {2227-7390},
year = {2021},
date = {2021-01-01},
journal = {Mathematics (Special issue on Recent Advances in {D}ata Science)},
volume = {9},
number = {1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Porras-Segovia, Alejandro; Cobo, Aurora; Díaz-Oliván, Isaac; Artés-Rodríguez, Antonio; Berrouiguet, Sofian; Lopez-Castroman, Jorge; Courtet, Philippe; Barrigón, Maria Luisa; Oquendo, María A; Baca-García, Enrique
Disturbed sleep as a clinical marker of wish to die: A smartphone monitoring study over three months of observation Artículo de revista
En: Journal of Affective Disorders, 2021, ISSN: 0165-0327.
Resumen | Enlaces | BibTeX | Etiquetas: Mhealth, Sleep, Smartphone, Suicide, Suicide attempt, Suicide ideation
@article{PORRASSEGOVIA2021,
title = {Disturbed sleep as a clinical marker of wish to die: A smartphone monitoring study over three months of observation},
author = {Alejandro Porras-Segovia and Aurora Cobo and Isaac D\'{i}az-Oliv\'{a}n and Antonio Art\'{e}s-Rodr\'{i}guez and Sofian Berrouiguet and Jorge Lopez-Castroman and Philippe Courtet and Maria Luisa Barrig\'{o}n and Mar\'{i}a A Oquendo and Enrique Baca-Garc\'{i}a},
url = {https://www.sciencedirect.com/science/article/pii/S0165032721001932},
doi = {https://doi.org/10.1016/j.jad.2021.02.059},
issn = {0165-0327},
year = {2021},
date = {2021-01-01},
journal = {Journal of Affective Disorders},
abstract = {Background
: Smartphone monitoring could contribute to the elucidation of the correlates of suicidal thoughts and behaviors (STB). In this study, we employ smartphone monitoring and machine learning techniques to explore the association of wish to die (passive suicidal ideation) with disturbed sleep, altered appetite and negative feelings.
Methods
: This is a prospective cohort study carried out among adult psychiatric outpatients with a history of STB. A daily questionnaire was administered through the MEmind smartphone application. Participants were followed-up for a median of 89.8 days, resulting in 9,878 person-days. Data analysis employed a machine learning technique called Indian Buffet Process.
Results
: 165 patients were recruited, 139 had the MEmind mobile application installed on their smartphone, and 110 answered questions regularly enough to be included in the final analysis. We found that the combination of wish to die and sleep problems was one of the most relevant latent features found across the sample, showing that these variables tend to be present during the same time frame (96 hours).
Conclusions
: Disturbed sleep emerges as a potential clinical marker for passive suicidal ideation. Our findings stress the importance of evaluating sleep as part of the screening for suicidal behavior. Compared to previous smartphone monitoring studies on suicidal behavior, this study includes a long follow-up period and a large sample.},
keywords = {Mhealth, Sleep, Smartphone, Suicide, Suicide attempt, Suicide ideation},
pubstate = {published},
tppubtype = {article}
}
Background
: Smartphone monitoring could contribute to the elucidation of the correlates of suicidal thoughts and behaviors (STB). In this study, we employ smartphone monitoring and machine learning techniques to explore the association of wish to die (passive suicidal ideation) with disturbed sleep, altered appetite and negative feelings.
Methods
: This is a prospective cohort study carried out among adult psychiatric outpatients with a history of STB. A daily questionnaire was administered through the MEmind smartphone application. Participants were followed-up for a median of 89.8 days, resulting in 9,878 person-days. Data analysis employed a machine learning technique called Indian Buffet Process.
Results
: 165 patients were recruited, 139 had the MEmind mobile application installed on their smartphone, and 110 answered questions regularly enough to be included in the final analysis. We found that the combination of wish to die and sleep problems was one of the most relevant latent features found across the sample, showing that these variables tend to be present during the same time frame (96 hours).
Conclusions
: Disturbed sleep emerges as a potential clinical marker for passive suicidal ideation. Our findings stress the importance of evaluating sleep as part of the screening for suicidal behavior. Compared to previous smartphone monitoring studies on suicidal behavior, this study includes a long follow-up period and a large sample.
: Smartphone monitoring could contribute to the elucidation of the correlates of suicidal thoughts and behaviors (STB). In this study, we employ smartphone monitoring and machine learning techniques to explore the association of wish to die (passive suicidal ideation) with disturbed sleep, altered appetite and negative feelings.
Methods
: This is a prospective cohort study carried out among adult psychiatric outpatients with a history of STB. A daily questionnaire was administered through the MEmind smartphone application. Participants were followed-up for a median of 89.8 days, resulting in 9,878 person-days. Data analysis employed a machine learning technique called Indian Buffet Process.
Results
: 165 patients were recruited, 139 had the MEmind mobile application installed on their smartphone, and 110 answered questions regularly enough to be included in the final analysis. We found that the combination of wish to die and sleep problems was one of the most relevant latent features found across the sample, showing that these variables tend to be present during the same time frame (96 hours).
Conclusions
: Disturbed sleep emerges as a potential clinical marker for passive suicidal ideation. Our findings stress the importance of evaluating sleep as part of the screening for suicidal behavior. Compared to previous smartphone monitoring studies on suicidal behavior, this study includes a long follow-up period and a large sample.
2020
Peis, Ignacio; López-Moríñigo, Javier-David; Perez-Rodriguez, Mercedes M; Barrigón, Maria Luisa; Ruiz-Gómez, Marta; Artés-Rodríguez, Antonio; Baca-García, Enrique
Actigraphic recording of motor activity in depressed inpatients: a novel computational approach to prediction of clinical course and hospital discharge Artículo de revista
En: Scientific Reports, vol. 10, no 17286, 2020.
Enlaces | BibTeX | Etiquetas: Actigraphic recording
@article{AArtes20h,
title = {Actigraphic recording of motor activity in depressed inpatients: a novel computational approach to prediction of clinical course and hospital discharge},
author = {Ignacio Peis and Javier-David L\'{o}pez-Mor\'{i}\~{n}igo and Mercedes M Perez-Rodriguez and Maria Luisa Barrig\'{o}n and Marta Ruiz-G\'{o}mez and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a },
doi = {https://doi.org/10.1038/s41598-020-74425-x},
year = {2020},
date = {2020-10-14},
journal = {Scientific Reports},
volume = {10},
number = {17286},
keywords = {Actigraphic recording},
pubstate = {published},
tppubtype = {article}
}
Carreras-García, Danae; Delgado-Gómez, David; Baca-García, Enrique; Artés-Rodríguez, Antonio
A Probabilistic Patient Scheduling Model with Time Variable Slots Artículo de revista
En: Computational and Mathematical Methods in Medicine, vol. 2020, no 9727096, pp. 10, 2020.
Enlaces | BibTeX | Etiquetas: e-health, patient scheduling systems, prediction theory
@article{AArtes20e,
title = {A Probabilistic Patient Scheduling Model with Time Variable Slots},
author = {Danae Carreras-Garc\'{i}a and David Delgado-G\'{o}mez and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {https://doi.org/10.1155/2020/9727096},
year = {2020},
date = {2020-09-01},
journal = {Computational and Mathematical Methods in Medicine},
volume = {2020},
number = {9727096},
pages = {10},
keywords = {e-health, patient scheduling systems, prediction theory},
pubstate = {published},
tppubtype = {article}
}
Carretero, Patricia; Campaña-Montes, Juan José; Artés-Rodríguez, Antonio
Ecological Momentary Assessment for Monitoring Risk of Suicide Behavior Artículo de revista
En: Current Topics in Behavioral Neurosciences, 2020.
Enlaces | BibTeX | Etiquetas: Big data, Digital footprint, Digital phenotype, e-health, Ecological momentary assessment, Machine learning, Mobile health, Suicidal risk, Wearable devices
@article{AArtes20b,
title = {Ecological Momentary Assessment for Monitoring Risk of Suicide Behavior},
author = {Patricia Carretero and Juan Jos\'{e} Campa\~{n}a-Montes and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {https://doi.org/10.1007/7854_2020_170},
year = {2020},
date = {2020-08-15},
journal = {Current Topics in Behavioral Neurosciences},
keywords = {Big data, Digital footprint, Digital phenotype, e-health, Ecological momentary assessment, Machine learning, Mobile health, Suicidal risk, Wearable devices},
pubstate = {published},
tppubtype = {article}
}
Ríos-Muñoz, Gonzalo; Artés-Rodríguez, Antonio; Fernández-Avilés, Francisco; Arenal, Ángel
Real-Time Ventricular Cancellation in Unipolar Atrial Fibrillation Electrograms Artículo de revista
En: Frontiers in Bioengineering and Biotechnology, vol. 8, no 789, 2020.
Enlaces | BibTeX | Etiquetas: atrial fibrillation, biomedical signal processing, multi-electrode catheter, real-time, unipolar electrograms
@article{AArtes20d,
title = {Real-Time Ventricular Cancellation in Unipolar Atrial Fibrillation Electrograms},
author = {Gonzalo R\'{i}os-Mu\~{n}oz and Antonio Art\'{e}s-Rodr\'{i}guez and Francisco Fern\'{a}ndez-Avil\'{e}s and \'{A}ngel Arenal},
doi = {https://doi.org/10.3389/fbioe.2020.00789},
year = {2020},
date = {2020-07-30},
journal = {Frontiers in Bioengineering and Biotechnology},
volume = {8},
number = {789},
keywords = {atrial fibrillation, biomedical signal processing, multi-electrode catheter, real-time, unipolar electrograms},
pubstate = {published},
tppubtype = {article}
}
Arenas-Castañeda, Pavel E; Aroca, Fuensanta; Martinez-Nicolas, Ismael; Espíndola, Luis A Castillo; Barahona, Igor; Maya-Hernández, Cynthya; Hernández, Martha Miriam Lavana; Mirón, Paulo César Manrique; Barrera, Daniela Guadalupe Alvarado; Aguilar, Erik Treviño; Núñez, Axayácatl Barrios; Carlos, Giovanna De Jesus; Garcés, Anabel Vildosola; Mercado, Josselyne Flores; Barrigón, Maria Luisa; Artés-Rodríguez, Antonio; de Leon, Santiago; Molina-Pizarro, Cristian Antonio; Franco, Arsenio Rosado; Perez-Rodriguez, Mercedes M; Courtet, Philippe; Martínez-Alés, Gonzalo; Baca-García, Enrique
Universal mental health screening with a focus on suicidal behaviour using smartphones in a Mexican rural community: protocol for the SMART-SCREEN population-based survey Artículo de revista
En: BMJ Open 2020, vol. 10, no e035041, 2020.
Enlaces | BibTeX | Etiquetas: Mental Health, Smartphone, Suicidal behavior
@article{AArtes20f,
title = {Universal mental health screening with a focus on suicidal behaviour using smartphones in a Mexican rural community: protocol for the SMART-SCREEN population-based survey},
author = {Pavel E Arenas-Casta\~{n}eda and Fuensanta Aroca and Ismael Martinez-Nicolas and Luis A Castillo Esp\'{i}ndola and Igor Barahona and Cynthya Maya-Hern\'{a}ndez and Martha Miriam Lavana Hern\'{a}ndez and Paulo C\'{e}sar Manrique Mir\'{o}n and Daniela Guadalupe Alvarado Barrera and Erik Trevi\~{n}o Aguilar and Axay\'{a}catl Barrios N\'{u}\~{n}ez and Giovanna De Jesus Carlos and Anabel Vildosola Garc\'{e}s and Josselyne Flores Mercado and Maria Luisa Barrig\'{o}n and Antonio Art\'{e}s-Rodr\'{i}guez and Santiago de Leon and Cristian Antonio Molina-Pizarro and Arsenio Rosado Franco and Mercedes M Perez-Rodriguez and Philippe Courtet and Gonzalo Mart\'{i}nez-Al\'{e}s and Enrique Baca-Garc\'{i}a},
doi = {10.1136/bmjopen-2019-035041},
year = {2020},
date = {2020-07-19},
journal = {BMJ Open 2020},
volume = {10},
number = {e035041},
keywords = {Mental Health, Smartphone, Suicidal behavior},
pubstate = {published},
tppubtype = {article}
}
Porras-Segovia, Alejandro; Molina-Madueño, Rosa María; Berrouiguet, Sofian; López-Castromán, Jorge; Barrigón, Maria Luisa; Pérez-Rodríguez, María Sandra; Marco, José Heliodoro; Díaz-Oliván, Isaac; de León, Santiago; Courtet, Philippe; Artés-Rodríguez, Antonio; Baca-García, Enrique
Smartphone-based ecological momentary assessment (EMA) in psychiatric patients and student controls: A real-world feasibility study Artículo de revista
En: Journal of Affective Disorders, vol. 274, pp. 733-741, 2020.
Enlaces | BibTeX | Etiquetas: Ecological momentary assessment, Wearable devices
@article{AArtes20c,
title = {Smartphone-based ecological momentary assessment (EMA) in psychiatric patients and student controls: A real-world feasibility study},
author = {Alejandro Porras-Segovia and Rosa Mar\'{i}a Molina-Madue\~{n}o and Sofian Berrouiguet and Jorge L\'{o}pez-Castrom\'{a}n and Maria Luisa Barrig\'{o}n and Mar\'{i}a Sandra P\'{e}rez-Rodr\'{i}guez and Jos\'{e} Heliodoro Marco and Isaac D\'{i}az-Oliv\'{a}n and Santiago de Le\'{o}n and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
doi = {https://doi.org/10.1016/j.jad.2020.05.067},
year = {2020},
date = {2020-05-26},
urldate = {2020-05-26},
journal = {Journal of Affective Disorders},
volume = {274},
pages = {733-741},
keywords = {Ecological momentary assessment, Wearable devices},
pubstate = {published},
tppubtype = {article}
}
Lopez-Morinigo, Javier-David; Ruiz-Ruano, Verónica González; Martínez, Adela Sánchez Escribano; Barrigón, María Luisa; Mata-Iturralde, L.; Muñoz-Lorenzo, L.; Sánchez-Alonso, S.; Artés-Rodríguez, Antonio; David, Anthony S; Baca-García, Enrique
Study protocol of a randomised clinical trial testing whether metacognitive training can improve insight and clinical outcomes in schizophrenia Artículo de revista
En: BMC Psychiatry, vol. 20, no 30, 2020.
Enlaces | BibTeX | Etiquetas: Ecological momentary assessment, Insight, Metacognitive training, Schizophrenia spectrum disorders
@article{AArtes20,
title = {Study protocol of a randomised clinical trial testing whether metacognitive training can improve insight and clinical outcomes in schizophrenia},
author = {Javier-David Lopez-Morinigo and Ver\'{o}nica Gonz\'{a}lez Ruiz-Ruano and Adela S\'{a}nchez Escribano Mart\'{i}nez and Mar\'{i}a Luisa Barrig\'{o}n and L. Mata-Iturralde and L. Mu\~{n}oz-Lorenzo and S. S\'{a}nchez-Alonso and Antonio Art\'{e}s-Rodr\'{i}guez and Anthony S David and Enrique Baca-Garc\'{i}a },
doi = {https://doi.org/10.1186/s12888-020-2431-x},
year = {2020},
date = {2020-01-29},
urldate = {2020-01-29},
journal = {BMC Psychiatry},
volume = {20},
number = {30},
keywords = {Ecological momentary assessment, Insight, Metacognitive training, Schizophrenia spectrum disorders},
pubstate = {published},
tppubtype = {article}
}
Norbury, Agnes; Liu, Shelley; Campaña-Montes, Juan José; Romero-Medrano, Lorena; Barrigón, María Luisa; Smith, Emma; Artés-Rodríguez, Antonio; Baca-García, Enrique; Perez-Rodriguez, Mercedes M
Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients Artículo de revista
En: Molecular psychiatry, pp. 1–11, 2020.
BibTeX | Etiquetas:
@article{norbury2020social,
title = {Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients},
author = {Agnes Norbury and Shelley Liu and Juan Jos\'{e} Campa\~{n}a-Montes and Lorena Romero-Medrano and Mar\'{i}a Luisa Barrig\'{o}n and Emma Smith and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a and Mercedes M Perez-Rodriguez},
year = {2020},
date = {2020-01-01},
journal = {Molecular psychiatry},
pages = {1--11},
publisher = {Nature Publishing Group},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2019
Moreno-Pino, Fernando; Porras-Segovia, Alejandro; López-Esteban, Pilar; Artés-Rodríguez, Antonio; Baca-García, Enrique
Validation of Fitbit Charge 2 and Fitbit Alta HR Against Polysomnography for Assessing Sleep in Adults With Obstructive Sleep Apnea Artículo de revista
En: Journal of Clinical Sleep Medicine, vol. 15, no 11, pp. 1645-1653, 2019.
Enlaces | BibTeX | Etiquetas: e-health, sleep apnea, Sleep disorders, Wearables
@article{AArtes19d,
title = {Validation of Fitbit Charge 2 and Fitbit Alta HR Against Polysomnography for Assessing Sleep in Adults With Obstructive Sleep Apnea},
author = {Fernando Moreno-Pino and Alejandro Porras-Segovia and Pilar L\'{o}pez-Esteban and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
doi = {10.5664/jcsm.8032},
year = {2019},
date = {2019-11-15},
journal = {Journal of Clinical Sleep Medicine},
volume = {15},
number = {11},
pages = {1645-1653},
keywords = {e-health, sleep apnea, Sleep disorders, Wearables},
pubstate = {published},
tppubtype = {article}
}
Bonilla-Escribano, P; Ramírez, David; Sedano-Capdevila, Alba; Campaña-Montes, Juan Jose; Baca-García, Enrique; Courtet, Philippe; Artés-Rodríguez, Antonio
Assessment of e-social activity in psychiatric patients Artículo de revista
En: IEEE J. Biomedical and Health Informatics, vol. 23, no 6, pp. 2247-2256, 2019, ISSN: 2168-2194.
Enlaces | BibTeX | Etiquetas: E-social Activity, expectation-maximisation algorithm, maximum likelihood, mixture model, point processes
@article{Bonilla-Escribano2019,
title = {Assessment of e-social activity in psychiatric patients},
author = {P Bonilla-Escribano and David Ram\'{i}rez and Alba Sedano-Capdevila and Juan Jose Campa\~{n}a-Montes and Enrique Baca-Garc\'{i}a and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {10.1109/JBHI.2019.2918687},
issn = {2168-2194},
year = {2019},
date = {2019-11-01},
journal = {IEEE J. Biomedical and Health Informatics},
volume = {23},
number = {6},
pages = {2247-2256},
keywords = {E-social Activity, expectation-maximisation algorithm, maximum likelihood, mixture model, point processes},
pubstate = {published},
tppubtype = {article}
}
Berrouiguet, Sofian; Barrigón, María Luisa; López-Castromán, Jorge; Courtet, Philippe; Artés-Rodríguez, Antonio; Baca-García, Enrique
Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol Artículo de revista
En: BMC Psychiatry, vol. 19, no 277, 2019.
Enlaces | BibTeX | Etiquetas: Data Mining, sensors, Smartphone, Suicide, Wearables
@article{AArtes19c,
title = {Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol},
author = {Sofian Berrouiguet and Mar\'{i}a Luisa Barrig\'{o}n and Jorge L\'{o}pez-Castrom\'{a}n and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a },
doi = {https://doi.org/10.1186/s12888-019-2260-y},
year = {2019},
date = {2019-09-07},
journal = {BMC Psychiatry},
volume = {19},
number = {277},
keywords = {Data Mining, sensors, Smartphone, Suicide, Wearables},
pubstate = {published},
tppubtype = {article}
}
López-Castromán, Jorge; Leiva-Murillo, José M; Cegla-Schvartzman, Fanny; Blasco-Fontecilla, Hilario; García-Nieto, R; Artés-Rodríguez, Antonio; Morant-Ginestar, C; Courtet, Philippe; Blanco, Carlos; Aroca, Fuensanta; Baca-García, Enrique
Onset of schizophrenia diagnoses in a large clinical cohort Artículo de revista
En: Scientific Reports, vol. 9, no 9865, 2019.
Enlaces | BibTeX | Etiquetas: Schizoprenia diagnosis
@article{AArtes19b,
title = {Onset of schizophrenia diagnoses in a large clinical cohort},
author = {Jorge L\'{o}pez-Castrom\'{a}n and Jos\'{e} M Leiva-Murillo and Fanny Cegla-Schvartzman and Hilario Blasco-Fontecilla and R Garc\'{i}a-Nieto and Antonio Art\'{e}s-Rodr\'{i}guez and C Morant-Ginestar and Philippe Courtet and Carlos Blanco and Fuensanta Aroca and Enrique Baca-Garc\'{i}a},
doi = {https://doi.org/10.1038/s41598-019-46109-8},
year = {2019},
date = {2019-07-08},
journal = {Scientific Reports},
volume = {9},
number = {9865},
keywords = {Schizoprenia diagnosis},
pubstate = {published},
tppubtype = {article}
}
Peis, Ignacio; Olmos, Pablo M; Vera-Varela, Constanza; Barrigón, María Luisa; Courtet, Philippe; Baca-García, Enrique; Artes-Rodríguez, Antonio
Deep Sequential Models for Suicidal Ideation From Multiple Source Data Artículo de revista
En: IEEE Journal of Biomedical and Health Informatics, vol. 23, no 6, pp. 2286 - 2293, 2019.
Enlaces | BibTeX | Etiquetas: attention, Deep learning, EMA, RNN, Suicide
@article{AArtes19,
title = {Deep Sequential Models for Suicidal Ideation From Multiple Source Data},
author = {Ignacio Peis and Pablo M Olmos and Constanza Vera-Varela and Mar\'{i}a Luisa Barrig\'{o}n and Philippe Courtet and Enrique Baca-Garc\'{i}a and Antonio Artes-Rodr\'{i}guez},
doi = {10.1109/JBHI.2019.2919270},
year = {2019},
date = {2019-05-27},
journal = {IEEE Journal of Biomedical and Health Informatics},
volume = {23},
number = {6},
pages = {2286 - 2293},
keywords = {attention, Deep learning, EMA, RNN, Suicide},
pubstate = {published},
tppubtype = {article}
}
2018
Luengo, David; Ríos-Muñoz, Gonzalo; Elvira, Victor; Sánchez, Carlos; Artés-Rodríguez, Antonio
Hierarchical Algorithms for Causality Retrieval in Atrial Fibrillation Intracavitary Electrograms Artículo de revista
En: IEEE Journal of Biomedical and Health Informatics, vol. PP, 2018.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Luengo2018,
title = {Hierarchical Algorithms for Causality Retrieval in Atrial Fibrillation Intracavitary Electrograms},
author = {David Luengo and Gonzalo R\'{i}os-Mu\~{n}oz and Victor Elvira and Carlos S\'{a}nchez and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {10.1109/JBHI.2018.2805773},
year = {2018},
date = {2018-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
volume = {PP},
abstract = {Multi-channel intracavitary electrograms (EGMs) are acquired at the electrophysiology laboratory to guide radio frequency catheter ablation of patients suffering from atrial fibrillation (AF). These EGMs are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex fractionated electrograms). In this paper, we introduce two hierarchical algorithms to retrieve the causal interactions among these multiple EGMs. Both algorithms are based on Granger causality, but other causality measures can be easily incorporated. In both cases, they start by selecting a root node, but they differ on the way in which they explore the set of signals to determine their causeeffect relationships: either testing the full set of unexplored signals (GS-CaRe) or performing a local search only among the set of neighbor EGMs (LS-CaRe). The ensuing causal model provides important information about the propagation of the electrical signals inside the atria, uncovering wavefronts and activation patterns that can guide cardiologists towards candidate areas for catheter ablation. Numerical experiments, on both synthetic signals and annotated real-world signals, show the good performance of the two proposed approaches},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Multi-channel intracavitary electrograms (EGMs) are acquired at the electrophysiology laboratory to guide radio frequency catheter ablation of patients suffering from atrial fibrillation (AF). These EGMs are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex fractionated electrograms). In this paper, we introduce two hierarchical algorithms to retrieve the causal interactions among these multiple EGMs. Both algorithms are based on Granger causality, but other causality measures can be easily incorporated. In both cases, they start by selecting a root node, but they differ on the way in which they explore the set of signals to determine their causeeffect relationships: either testing the full set of unexplored signals (GS-CaRe) or performing a local search only among the set of neighbor EGMs (LS-CaRe). The ensuing causal model provides important information about the propagation of the electrical signals inside the atria, uncovering wavefronts and activation patterns that can guide cardiologists towards candidate areas for catheter ablation. Numerical experiments, on both synthetic signals and annotated real-world signals, show the good performance of the two proposed approaches
2016
Nazábal, Alfredo; Garcia-Moreno, Pablo; Artés-Rodríguez, Antonio; Ghahramani, Zoubin
Human Activity Recognition by Combining a Small Number of Classifiers. Artículo de revista
En: IEEE journal of biomedical and health informatics, vol. 20, no 5, pp. 1342 – 1351, 2016, ISSN: 2168-2208.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian inference, Biological system modeling, Classifier combination, Databases, Estimation, Hidden Markov models, Journal, Sensor systems
@article{Nazabal2016b,
title = {Human Activity Recognition by Combining a Small Number of Classifiers.},
author = {Alfredo Naz\'{a}bal and Pablo Garcia-Moreno and Antonio Art\'{e}s-Rodr\'{i}guez and Zoubin Ghahramani},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7161292},
doi = {10.1109/JBHI.2015.2458274},
issn = {2168-2208},
year = {2016},
date = {2016-09-01},
journal = {IEEE journal of biomedical and health informatics},
volume = {20},
number = {5},
pages = {1342 -- 1351},
publisher = {IEEE},
abstract = {We consider the problem of daily Human Activity Recognition (HAR) using multiple wireless inertial sensors and, specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semi-supervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and a Markovian structure of the human activities.},
keywords = {Bayes methods, Bayesian inference, Biological system modeling, Classifier combination, Databases, Estimation, Hidden Markov models, Journal, Sensor systems},
pubstate = {published},
tppubtype = {article}
}
We consider the problem of daily Human Activity Recognition (HAR) using multiple wireless inertial sensors and, specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semi-supervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and a Markovian structure of the human activities.
Nazabal, Alfredo; Garcia-Moreno, Pablo; Artes-Rodriguez, Antonio; Ghahramani, Zoubin
Human Activity Recognition by Combining a Small Number of Classifiers Artículo de revista
En: IEEE journal of biomedical and health informatics, vol. To appear, 2016, ISSN: 2168-2208.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian inference, Biological system modeling, Classifier combination, Databases, Estimation, Hidden Markov models, Sensor systems
@article{Nazabal2016bb,
title = {Human Activity Recognition by Combining a Small Number of Classifiers},
author = {Alfredo Nazabal and Pablo Garcia-Moreno and Antonio Artes-Rodriguez and Zoubin Ghahramani},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7161292},
doi = {10.1109/JBHI.2015.2458274},
issn = {2168-2208},
year = {2016},
date = {2016-01-01},
journal = {IEEE journal of biomedical and health informatics},
volume = {To appear},
publisher = {IEEE},
abstract = {We consider the problem of daily Human Activity Recognition (HAR) using multiple wireless inertial sensors and, specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semi-supervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and a Markovian structure of the human activities.},
keywords = {Bayes methods, Bayesian inference, Biological system modeling, Classifier combination, Databases, Estimation, Hidden Markov models, Sensor systems},
pubstate = {published},
tppubtype = {article}
}
We consider the problem of daily Human Activity Recognition (HAR) using multiple wireless inertial sensors and, specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semi-supervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and a Markovian structure of the human activities.
2015
Garcia-Moreno, Pablo; Teh, Yee Whye; Perez-Cruz, Fernando; Artés-Rodríguez, Antonio
Bayesian Nonparametric Crowdsourcing Artículo de revista
En: Journal of Machine Learning Research, vol. 16, no August, pp. 1607–1627, 2015.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian nonparametrics, Dirichlet process, Gibbs sampling, Hierarchical clustering, Journal, Multiple annotators
@article{Moreno2015b,
title = {Bayesian Nonparametric Crowdsourcing},
author = {Pablo Garcia-Moreno and Yee Whye Teh and Fernando Perez-Cruz and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.jmlr.org/papers/volume16/moreno15a/moreno15a.pdf},
year = {2015},
date = {2015-08-01},
journal = {Journal of Machine Learning Research},
volume = {16},
number = {August},
pages = {1607--1627},
abstract = {Crowdsourcing has been proven to be an effective and efficient tool to annotate large datasets. User annotations are often noisy, so methods to combine the annotations to produce reliable estimates of the ground truth are necessary. We claim that considering the existence of clusters of users in this combination step can improve the performance. This is especially important in early stages of crowdsourcing implementations, where the number of annotations is low. At this stage there is not enough information to accurately estimate the bias introduced by each annotator separately, so we have to resort to models that consider the statistical links among them. In addition, finding these clusters is interesting in itself as knowing the behavior of the pool of annotators allows implementing efficient active learning strategies. Based on this, we propose in this paper two new fully unsupervised models based on a Chinese Restaurant Process (CRP) prior and a hierarchical structure that allows inferring these groups jointly with the ground truth and the properties of the users. Efficient inference algorithms based on Gibbs sampling with auxiliary variables are proposed. Finally, we perform experiments, both on synthetic and real databases, to show the advantages of our models over state-of-the-art algorithms.},
keywords = {Bayesian nonparametrics, Dirichlet process, Gibbs sampling, Hierarchical clustering, Journal, Multiple annotators},
pubstate = {published},
tppubtype = {article}
}
Crowdsourcing has been proven to be an effective and efficient tool to annotate large datasets. User annotations are often noisy, so methods to combine the annotations to produce reliable estimates of the ground truth are necessary. We claim that considering the existence of clusters of users in this combination step can improve the performance. This is especially important in early stages of crowdsourcing implementations, where the number of annotations is low. At this stage there is not enough information to accurately estimate the bias introduced by each annotator separately, so we have to resort to models that consider the statistical links among them. In addition, finding these clusters is interesting in itself as knowing the behavior of the pool of annotators allows implementing efficient active learning strategies. Based on this, we propose in this paper two new fully unsupervised models based on a Chinese Restaurant Process (CRP) prior and a hierarchical structure that allows inferring these groups jointly with the ground truth and the properties of the users. Efficient inference algorithms based on Gibbs sampling with auxiliary variables are proposed. Finally, we perform experiments, both on synthetic and real databases, to show the advantages of our models over state-of-the-art algorithms.
Luengo, David; Monzon, Sandra; Trigano, Tom; Vía, Javier; Artés-Rodríguez, Antonio
Blind Analysis of Atrial Fibrillation Electrograms: A Sparsity-Aware Formulation Artículo de revista
En: Integrated Computer-Aided Engineering, vol. 22, no 1, pp. 71–85, 2015.
Resumen | Enlaces | BibTeX | Etiquetas: atrial fibrillation, biomedical signal processing
@article{Luengo2014bb,
title = {Blind Analysis of Atrial Fibrillation Electrograms: A Sparsity-Aware Formulation},
author = {David Luengo and Sandra Monzon and Tom Trigano and Javier V\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://content.iospress.com/articles/integrated-computer-aided-engineering/ica00471
http://www.tsc.uc3m.es/~dluengo/sparseEGM.pdf},
year = {2015},
date = {2015-01-01},
journal = {Integrated Computer-Aided Engineering},
volume = {22},
number = {1},
pages = {71--85},
abstract = {The problem of blind sparse analysis of electrogram (EGM) signals under atrial fibrillation (AF) conditions is considered in this paper. A mathematical model for the observed signals that takes into account the multiple foci typically appearing inside the heart during AF is firstly introduced. Then, a reconstruction model based on a fixed dictionary is developed and several alternatives for choosing the dictionary are discussed. In order to obtain a sparse solution, which takes into account the biological restrictions of the problem at the same time, the paper proposes using a Least Absolute Shrinkage and Selection Operator (LASSO) regularization followed by a post-processing stage that removes low amplitude coefficients violating the refractory period characteristic of cardiac cells. Finally, spectral analysis is performed on the clean activation sequence obtained from the sparse learning stage in order to estimate the number of latent foci and their frequencies. Simulations on synthetic signals and applications on real data are provided to validate the proposed approach.},
keywords = {atrial fibrillation, biomedical signal processing},
pubstate = {published},
tppubtype = {article}
}
The problem of blind sparse analysis of electrogram (EGM) signals under atrial fibrillation (AF) conditions is considered in this paper. A mathematical model for the observed signals that takes into account the multiple foci typically appearing inside the heart during AF is firstly introduced. Then, a reconstruction model based on a fixed dictionary is developed and several alternatives for choosing the dictionary are discussed. In order to obtain a sparse solution, which takes into account the biological restrictions of the problem at the same time, the paper proposes using a Least Absolute Shrinkage and Selection Operator (LASSO) regularization followed by a post-processing stage that removes low amplitude coefficients violating the refractory period characteristic of cardiac cells. Finally, spectral analysis is performed on the clean activation sequence obtained from the sparse learning stage in order to estimate the number of latent foci and their frequencies. Simulations on synthetic signals and applications on real data are provided to validate the proposed approach.
2014
Santiago-Mozos, Ricardo; Perez-Cruz, Fernando; Madden, Michael; Artés-Rodríguez, Antonio
An Automated Screening System for Tuberculosis Artículo de revista
En: IEEE journal of biomedical and health informatics, vol. 18, no 3, pp. 855-862, 2014, ISSN: 2168-2208.
Resumen | Enlaces | BibTeX | Etiquetas: Automated screening, Bayesian, Decision making, Sequential analysis, Tuberculosis
@article{Santiago-Mozos2013,
title = {An Automated Screening System for Tuberculosis},
author = {Ricardo Santiago-Mozos and Fernando Perez-Cruz and Michael Madden and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P47_2014_An Automated Screening System for Tuberculosis.pdf http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6630069},
issn = {2168-2208},
year = {2014},
date = {2014-05-01},
journal = {IEEE journal of biomedical and health informatics},
volume = {18},
number = {3},
pages = {855-862},
publisher = {IEEE},
abstract = {Automated screening systems are commonly used to detect some agent in a sample and take a global decision about the subject (e.g. ill/healthy) based on these detections. We propose a Bayesian methodology for taking decisions in (sequential) screening systems that considers the false alarm rate of the detector. Our approach assesses the quality of its decisions and provides lower bounds on the achievable performance of the screening system from the training data. In addition, we develop a complete screening system for sputum smears in tuberculosis diagnosis, and show, using a real-world database, the advantages of the proposed framework when compared to the commonly used count detections and threshold approach.},
keywords = {Automated screening, Bayesian, Decision making, Sequential analysis, Tuberculosis},
pubstate = {published},
tppubtype = {article}
}
Automated screening systems are commonly used to detect some agent in a sample and take a global decision about the subject (e.g. ill/healthy) based on these detections. We propose a Bayesian methodology for taking decisions in (sequential) screening systems that considers the false alarm rate of the detector. Our approach assesses the quality of its decisions and provides lower bounds on the achievable performance of the screening system from the training data. In addition, we develop a complete screening system for sputum smears in tuberculosis diagnosis, and show, using a real-world database, the advantages of the proposed framework when compared to the commonly used count detections and threshold approach.
Piñeiro-Ave, José; Blanco-Velasco, Manuel; Cruz-Roldán, Fernando; Artés-Rodríguez, Antonio
Target Detection for Low Cost Uncooled MWIR Cameras Based on Empirical Mode Decomposition Artículo de revista
En: Infrared Physics &amp; Technology, vol. 63, pp. 222–231, 2014, ISSN: 13504495.
Resumen | Enlaces | BibTeX | Etiquetas: Background subtraction, Change detection, Denoising, Drift, Empirical Mode Decomposition (EMD), Intrinsic Mode Function (IMF)
@article{Pineiro-Ave2014,
title = {Target Detection for Low Cost Uncooled MWIR Cameras Based on Empirical Mode Decomposition},
author = {Jos\'{e} Pi\~{n}eiro-Ave and Manuel Blanco-Velasco and Fernando Cruz-Rold\'{a}n and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P49_2014_Target Detection for Low Cost Uncooled MWIR Cameras Based on Empirical Mode Decomposition.pdf
http://www.sciencedirect.com/science/article/pii/S1350449514000085},
issn = {13504495},
year = {2014},
date = {2014-01-01},
journal = {Infrared Physics \& Technology},
volume = {63},
pages = {222--231},
abstract = {In this work, a novel method for detecting low intensity fast moving objects with low cost Medium Wavelength Infrared (MWIR) cameras is proposed. The method is based on background subtraction in a video sequence obtained with a low density Focal Plane Array (FPA) of the newly available uncooled lead selenide (PbSe) detectors. Thermal instability along with the lack of specific electronics and mechanical devices for canceling the effect of distortion make background image identification very difficult. As a result, the identification of targets is performed in low signal to noise ratio (SNR) conditions, which may considerably restrict the sensitivity of the detection algorithm. These problems are addressed in this work by means of a new technique based on the empirical mode decomposition, which accomplishes drift estimation and target detection. Given that background estimation is the most important stage for detecting, a previous denoising step enabling a better drift estimation is designed. Comparisons are conducted against a denoising technique based on the wavelet transform and also with traditional drift estimation methods such as Kalman filtering and running average. The results reported by the simulations show that the proposed scheme has superior performance.},
keywords = {Background subtraction, Change detection, Denoising, Drift, Empirical Mode Decomposition (EMD), Intrinsic Mode Function (IMF)},
pubstate = {published},
tppubtype = {article}
}
In this work, a novel method for detecting low intensity fast moving objects with low cost Medium Wavelength Infrared (MWIR) cameras is proposed. The method is based on background subtraction in a video sequence obtained with a low density Focal Plane Array (FPA) of the newly available uncooled lead selenide (PbSe) detectors. Thermal instability along with the lack of specific electronics and mechanical devices for canceling the effect of distortion make background image identification very difficult. As a result, the identification of targets is performed in low signal to noise ratio (SNR) conditions, which may considerably restrict the sensitivity of the detection algorithm. These problems are addressed in this work by means of a new technique based on the empirical mode decomposition, which accomplishes drift estimation and target detection. Given that background estimation is the most important stage for detecting, a previous denoising step enabling a better drift estimation is designed. Comparisons are conducted against a denoising technique based on the wavelet transform and also with traditional drift estimation methods such as Kalman filtering and running average. The results reported by the simulations show that the proposed scheme has superior performance.
O'Mahony, Niamh; Florentino-Liaño, Blanca; Carballo, Juan J; Baca-García, Enrique; Artés-Rodríguez, Antonio
Objective diagnosis of ADHD using IMUs Artículo de revista
En: Medical engineering &amp; physics, vol. 36, no 7, pp. 922–6, 2014, ISSN: 1873-4030.
Resumen | Enlaces | BibTeX | Etiquetas: Attention deficit/hyperactivity disorder, Classification, Inertial sensors, Machine learning, Objective diagnosis
@article{O'Mahony2014,
title = {Objective diagnosis of ADHD using IMUs},
author = {Niamh O'Mahony and Blanca Florentino-Lia\~{n}o and Juan J Carballo and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P50_2014_Objective Diagnosis of ADHD Using IMUs.pdf
http://www.sciencedirect.com/science/article/pii/S1350453314000459},
issn = {1873-4030},
year = {2014},
date = {2014-01-01},
journal = {Medical engineering \& physics},
volume = {36},
number = {7},
pages = {922--6},
abstract = {This work proposes the use of miniature wireless inertial sensors as an objective tool for the diagnosis of ADHD. The sensors, consisting of both accelerometers and gyroscopes to measure linear and rotational movement, respectively, are used to characterize the motion of subjects in the setting of a psychiatric consultancy. A support vector machine is used to classify a group of subjects as either ADHD or non-ADHD and a classification accuracy of greater than 95% has been achieved. Separate analyses of the motion data recorded during various activities throughout the visit to the psychiatric consultancy show that motion recorded during a continuous performance test (a forced concentration task) provides a better classification performance than that recorded during "free time".},
keywords = {Attention deficit/hyperactivity disorder, Classification, Inertial sensors, Machine learning, Objective diagnosis},
pubstate = {published},
tppubtype = {article}
}
This work proposes the use of miniature wireless inertial sensors as an objective tool for the diagnosis of ADHD. The sensors, consisting of both accelerometers and gyroscopes to measure linear and rotational movement, respectively, are used to characterize the motion of subjects in the setting of a psychiatric consultancy. A support vector machine is used to classify a group of subjects as either ADHD or non-ADHD and a classification accuracy of greater than 95% has been achieved. Separate analyses of the motion data recorded during various activities throughout the visit to the psychiatric consultancy show that motion recorded during a continuous performance test (a forced concentration task) provides a better classification performance than that recorded during "free time".
Montoya-Martinez, Jair; Artés-Rodríguez, Antonio; Pontil, Massimiliano; Hansen, Lars Kai
A Regularized Matrix Factorization Approach to Induce Structured Sparse-Low Rank Solutions in the EEG Inverse Problem Artículo de revista
En: EURASIP Journal on Advances in Signal Processing, vol. 2014, no 1, pp. 97, 2014, ISSN: 1687-6180.
Resumen | Enlaces | BibTeX | Etiquetas: Low rank, Matrix factorization, Nonsmooth-nonconvex optimization, Regularization, Structured sparsity
@article{Montoya-Martinez2014b,
title = {A Regularized Matrix Factorization Approach to Induce Structured Sparse-Low Rank Solutions in the EEG Inverse Problem},
author = {Jair Montoya-Martinez and Antonio Art\'{e}s-Rodr\'{i}guez and Massimiliano Pontil and Lars Kai Hansen},
url = {http://www.tsc.uc3m.es/~antonio/papers/P48_2014_A Regularized Matrix Factorization Approach to Induce Structured Sparse-Low Rank Solutions in the EEG Inverse Problem.pdf
http://asp.eurasipjournals.com/content/2014/1/97/abstract},
issn = {1687-6180},
year = {2014},
date = {2014-01-01},
journal = {EURASIP Journal on Advances in Signal Processing},
volume = {2014},
number = {1},
pages = {97},
publisher = {Springer},
abstract = {We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy Electroencephalographic (EEG) measurements, commonly named as the EEG inverse problem. We propose a new method to induce neurophysiological meaningful solutions, which takes into account the smoothness, structured sparsity and low rank of the BES matrix. The method is based on the factorization of the BES matrix as a product of a sparse coding matrix and a dense latent source matrix. The structured sparse-low rank structure is enforced by minimizing a regularized functional that includes the l21-norm of the coding matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We analyze the convergence of the optimization procedure, and we compare, under different synthetic scenarios, the performance of our method respect to the Group Lasso and Trace Norm regularizers when they are applied directly to the target matrix.},
keywords = {Low rank, Matrix factorization, Nonsmooth-nonconvex optimization, Regularization, Structured sparsity},
pubstate = {published},
tppubtype = {article}
}
We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy Electroencephalographic (EEG) measurements, commonly named as the EEG inverse problem. We propose a new method to induce neurophysiological meaningful solutions, which takes into account the smoothness, structured sparsity and low rank of the BES matrix. The method is based on the factorization of the BES matrix as a product of a sparse coding matrix and a dense latent source matrix. The structured sparse-low rank structure is enforced by minimizing a regularized functional that includes the l21-norm of the coding matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We analyze the convergence of the optimization procedure, and we compare, under different synthetic scenarios, the performance of our method respect to the Group Lasso and Trace Norm regularizers when they are applied directly to the target matrix.
2013
Serrano-Drozdowskyj, E; López-Castromán, Jorge; Leiva-Murillo, Jose M; Blasco-Fontecilla, Hilario; Garcia-Nieto, R; Artés-Rodríguez, Antonio; Morant-Ginestar, C; Blanco, Carlos; Courtet, Philippe; Baca-García, Enrique
1533 – A Naturalistic Study of the Diagnostic Evolution of Schizophrenia Artículo de revista
En: European Psychiatry, vol. 28, 2013.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Serrano-Drozdowskyj2013,
title = {1533 \textendash A Naturalistic Study of the Diagnostic Evolution of Schizophrenia},
author = {E Serrano-Drozdowskyj and Jorge L\'{o}pez-Castrom\'{a}n and Jose M Leiva-Murillo and Hilario Blasco-Fontecilla and R Garcia-Nieto and Antonio Art\'{e}s-Rodr\'{i}guez and C Morant-Ginestar and Carlos Blanco and Philippe Courtet and Enrique Baca-Garc\'{i}a},
url = {http://www.sciencedirect.com/science/article/pii/S0924933813765465},
year = {2013},
date = {2013-01-01},
journal = {European Psychiatry},
volume = {28},
abstract = {INTRODUCTION In the absence of biological measures, diagnostic long-term stability provides the best evidence of diagnostic validity.Therefore,the study of diagnostic stability in naturalistic conditions may reflect clinical validity and utility of current schizophrenia diagnostic criteria. OBJECTIVES Describe the diagnostic evolution of schizophrenia in clinical settings. METHODS We examined the stability of schizophrenia first diagnoses (n=26,163) in public mental health centers of Madrid (Spain).Probability of maintaining the diagnosis of schizophrenia was calculated considering the cumulative percentage of each diagnosis per month during 48 months after the initial diagnosis of schizophrenia. RESULTS 65% of the subjects kept the diagnosis of schizophrenia in subsequent assessments (Figure 1). Patients who changed (35%) did so in the first 4-8 months. After that time gap the rates of each diagnostic category remained stable. Diagnostic shift from schizophrenia was more commonly toward the following diagnoses: personality disorders (F60), delusional disorders (F22), bipolar disorder (F31), persistent mood disorders (F34), acute and transient psychotic disorders (F23) or schizoaffective disorder (F25). CONCLUSIONS Once it is confirmed, clinical assessment repeatedly maintains the diagnosis of schizophrenia.The time lapse for its confirmation agrees with the current diagnostic criteria in DSM-IV. We will discuss the implications of these findings for the categorical versus dimensional debate in the diagnosis of schizophrenia.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
INTRODUCTION In the absence of biological measures, diagnostic long-term stability provides the best evidence of diagnostic validity.Therefore,the study of diagnostic stability in naturalistic conditions may reflect clinical validity and utility of current schizophrenia diagnostic criteria. OBJECTIVES Describe the diagnostic evolution of schizophrenia in clinical settings. METHODS We examined the stability of schizophrenia first diagnoses (n=26,163) in public mental health centers of Madrid (Spain).Probability of maintaining the diagnosis of schizophrenia was calculated considering the cumulative percentage of each diagnosis per month during 48 months after the initial diagnosis of schizophrenia. RESULTS 65% of the subjects kept the diagnosis of schizophrenia in subsequent assessments (Figure 1). Patients who changed (35%) did so in the first 4-8 months. After that time gap the rates of each diagnostic category remained stable. Diagnostic shift from schizophrenia was more commonly toward the following diagnoses: personality disorders (F60), delusional disorders (F22), bipolar disorder (F31), persistent mood disorders (F34), acute and transient psychotic disorders (F23) or schizoaffective disorder (F25). CONCLUSIONS Once it is confirmed, clinical assessment repeatedly maintains the diagnosis of schizophrenia.The time lapse for its confirmation agrees with the current diagnostic criteria in DSM-IV. We will discuss the implications of these findings for the categorical versus dimensional debate in the diagnosis of schizophrenia.