@article{Bl\'{a}zquez-S\'{a}nchez2025b,

title = {Automated web-based typing of Clostridioides difficile ribotypes via MALDI-TOF MS},

author = {Mario Bl\'{a}zquez-S\'{a}nchez and Alejandro Guerrero-L\'{o}pez and Ana Candela and Albert Belenguer-Llorens and Jos\'{e} Miguel Moreno and Carlos Sevilla-Salcedo and Mar\'{i}a S\'{a}nchez-Cueto and Manuel J. Arroyo and Mark Guti\'{e}rrez-Pareja and Vanessa G\'{o}mez-Verdejo and Pablo M. Olmos and Luis Mancera and Patricia Mu\~{n}oz and Mercedes Mar\'{i}n and Luis Alcal\'{a} and David Rodr\'{i}guez-Temporal and Bel\'{e}n Rodr\'{i}guez-S\'{a}nchez},

doi = {10.1186/s12859-025-06200-6},

issn = {1471-2105},

year  = {2025},

date = {2025-06-19},

journal = {BMC Bioinformatics},

volume = {26},

number = {1},

publisher = {Springer Science and Business Media LLC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:doi/10.2196/63962,

title = {Emotion Forecasting: A Transformer-Based Approach},

author = {Leire Paz-Arbaizar and Jorge Lopez-Castroman and Antonio Art\'{e}s-Rodr\'{i}guez and Pablo M Olmos and David Ram\'{i}rez},

url = {https://doi.org/10.2196/63962},

doi = {10.2196/63962},

issn = {1438-8871},

year  = {2025},

date = {2025-03-18},

journal = {J Med Internet Res},

volume = {27},

pages = {e63962},

abstract = {Background: Monitoring the emotional states of patients with psychiatric problems has always been challenging due to the noncontinuous nature of clinical assessments, the effect of the health care environment, and the inherent subjectivity of evaluation instruments. However, mental states in psychiatric disorders exhibit substantial variability over time, making real-time monitoring crucial for preventing risky situations and ensuring appropriate treatment. Objective: This study aimed to leverage new technologies and deep learning techniques to enable more objective, real-time monitoring of patients. This was achieved by passively monitoring variables such as step count, patient location, and sleep patterns using mobile devices. We aimed to predict patient self-reports and detect sudden variations in their emotional valence, identifying situations that may require clinical intervention. Methods: Data for this project were collected using the Evidence-Based Behavior (eB2) app, which records both passive and self-reported variables daily. Passive data refer to behavioral information gathered via the eB2 app through sensors embedded in mobile devices and wearables. These data were obtained from studies conducted in collaboration with hospitals and clinics that used eB2. We used hidden Markov models (HMMs) to address missing data and transformer deep neural networks for time-series forecasting. Finally, classification algorithms were applied to predict several variables, including emotional state and responses to the Patient Health Questionnaire-9. Results: Through real-time patient monitoring, we demonstrated the ability to accurately predict patients' emotional states and anticipate changes over time. Specifically, our approach achieved high accuracy (0.93) and a receiver operating characteristic (ROC) area under the curve (AUC) of 0.98 for emotional valence classification. For predicting emotional state changes 1 day in advance, we obtained an ROC AUC of 0.87. Furthermore, we demonstrated the feasibility of forecasting responses to the Patient Health Questionnaire-9, with particularly strong performance for certain questions. For example, in question 9, related to suicidal ideation, our model achieved an accuracy of 0.9 and an ROC AUC of 0.77 for predicting the next day's response. Moreover, we illustrated the enhanced stability of multivariate time-series forecasting when HMM preprocessing was combined with a transformer model, as opposed to other time-series forecasting methods, such as recurrent neural networks or long short-term memory cells. Conclusions: The stability of multivariate time-series forecasting improved when HMM preprocessing was combined with a transformer model, as opposed to other time-series forecasting methods (eg, recurrent neural network and long short-term memory), leveraging the attention mechanisms to capture longer time dependencies and gain interpretability. We showed the potential to assess the emotional state of a patient and the scores of psychiatric questionnaires from passive variables in advance. This allows real-time monitoring of patients and hence better risk detection and treatment adjustment.},

keywords = {affect; emotional valence; machine learning; mental disorder; monitoring; mood; passive data; Patient Health Questionnaire-9; PHQ-9; psychological distress; time-series forecasting},

pubstate = {published},

tppubtype = {article}

}

@article{Salom2025DigitalRelapse,

title = {Digital relapse prevention plan for substance use disorders: study protocol for a multicentre randomised controlled trial},

author = {R. Salom and \'{A}. Pico Rada and J. J. Mu\~{n}oz Garc\'{i}a and H. Garc\'{i}a-Mieres and A. Art\'{e}s-Rodr\'{i}guez},

doi = {10.1136/bmjhci-2025-101808},

year  = {2025},

date = {2025-01-01},

journal = {BMJ Health \& Care Informatics},

volume = {32},

pages = {e101808},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{johnson2025optimal,

title = {Optimal anytime online learning on $ell_p$-balls},

author = {Emmeran Johnson and David Mart\'{i}nez-Rubio and Ciara Pike-Burke and Patrick Rebeschini},

url = {https://arxiv.org/abs/2506.19752},

year  = {2025},

date = {2025-01-01},

journal = {arXiv preprint arXiv:2506.19752},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{roux2025implicit,

title = {Implicit Riemannian Optimism with Applications to Min-Max Problems},

author = {Christophe Roux and David Mart\'{i}nez-Rubio and Sebastian Pokutta},

url = {https://arxiv.org/abs/2501.18381},

year  = {2025},

date = {2025-01-01},

journal = {arXiv preprint arXiv:2501.18381},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hendrych2025secant,

title = {Secant Line Search for Frank-Wolfe Algorithms},

author = {Deborah Hendrych and Sebastian Pokutta and Mathieu Besan\c{c}on and David Mart\'{i}nez-Rubio},

url = {https://arxiv.org/abs/2501.18775},

year  = {2025},

date = {2025-01-01},

journal = {arXiv preprint arXiv:2501.18775},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{11078847,

title = {On the Second-Order Asymptotics of the Hoeffding Test and Other Divergence Tests},

author = {K. V. Harsha and Jithin Ravi and Tobias Koch},

doi = {10.1109/TIT.2025.3588194},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {71},

number = {10},

pages = {7459-7483},

keywords = {Testing;Zinc;Random variables;Training;Information theory;Seminars;Europe;Error probability;Data mining;Convergence;Composite hypothesis testing;divergence;divergence test;generalized likelihood ratio test;Hoeffding test;hypothesis test;second-order asymptotics},

pubstate = {published},

tppubtype = {article}

}

@article{BELENGUERLLORENS2025111887,

title = {Unified Bayesian representation for high-dimensional multi-modal biomedical data for small-sample classification},

author = {Albert Belenguer-Llorens and Carlos Sevilla-Salcedo and Jussi Tohka and Vanessa G\'{o}mez-Verdejo},

url = {https://www.sciencedirect.com/science/article/pii/S0952197625018895},

doi = {https://doi.org/10.1016/j.engappai.2025.111887},

issn = {0952-1976},

year  = {2025},

date = {2025-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {160},

pages = {111887},

abstract = {The increasing availability of multi-modal medical data, including neuroimaging, genetic profiles, and clinical measurements, offers unprecedented opportunities for advancing disease diagnosis and prognosis. However, integrating these heterogeneous data sources poses significant challenges due to their high dimensionality, redundancy, and small sample sizes, which hinder the effectiveness of traditional machine learning models. To overcome these challenges, we present the BAyesian Latent Data Unified Representation model (BALDUR), a novel Bayesian algorithm designed to deal with multi-modal datasets and small sample sizes in high-dimensional settings while providing explainable solutions. To do so, the proposed model combines within a common latent space the different data views to extract the relevant information to solve the classification task and prune out the irrelevant/redundant features/data views. Furthermore, to provide generalizable solutions in small sample size scenarios, BALDUR efficiently integrates dual kernels over the views with a small sample-to-feature ratio. Finally, its linear nature ensures the explainability of the model outcomes, allowing its use for biomarker identification. This model was tested over two different neurodegeneration datasets, outperforming the state-of-the-art models and detecting features aligned with markers already described in the scientific literature.},

keywords = {Bayesian modeling, Machine learning health applications, Multi-modal data, Wide-data},

pubstate = {published},

tppubtype = {article}

}

@article{CONEJERO2025184,

title = {Feasibility of digital monitoring in patients receiving ambulatory psychiatric care},

author = {Ismael Conejero and Miguel \'{A}ngel Villalba and Victoria Matos and Oscar Jim\'{e}nez and Rafael Garc\'{i}a and Luc\'{i}a Albarrac\'{i}n-Garc\'{i}a and Alejandro Porras-Segovia and Mar\'{i}a Luisa Barrig\'{o}n and Jorge Lopez-Castroman and Antonio Art\'{e}s-Rodriguez and Enrique Baca-Garcia},

url = {https://www.sciencedirect.com/science/article/pii/S0022395625003723},

doi = {https://doi.org/10.1016/j.jpsychires.2025.05.072},

issn = {0022-3956},

year  = {2025},

date = {2025-01-01},

journal = {Journal of Psychiatric Research},

volume = {189},

pages = {184-192},

abstract = {Objective 

Monitoring the course of mental disorders in patients receiving ambulatory care may help improve their outcome and reduce complications. For this purpose, smartphone based digital monitoring has been suggested as an effective approach. However, few data exist regarding its feasibility in real-world. In this naturalistic study, we investigated the feasibility of digital monitoring in patients receiving routine ambulatory care and its clinical determinants. 

Methods 

The clinical characteristics of patients were collected from the electronic clinical records. They included age, gender, diagnosis according to the International Statistical Classification of Diseases and Related Health Problems 10th criteria, and the measurement of severity according to the Clinical Global Impression-Severity scale. Engagement with digital monitoring at baseline was assessed by measuring the installation rate of the eB2 MindCare application on smartphones. The retention time in the digital monitoring (i.e., time until uninstallation and duration of use) was assessed over a 90-day follow-up period. 

Results 

Among the 212 patients involved in the ambulatory routine clinical follow-up, 148 installed the eB2 MindCare application on their smartphone. Most patients (82.5 %) were retained in the digital monitoring over the 90 days follow-up period. The engagement in the digital monitoring at baseline did not depend upon the clinical characteristics of patients (including age, gender, diagnosis or severity). The overall retention in the digital monitoring was not affected by those clinical characteristics. A diagnosis of impulsive disorder was associated with earlier interruption of the digital monitoring only during the first 90-day period. 

Conclusion 

Clinical characteristics do not influence engagement in digital monitoring or overall retention. Meanwhile, the diagnosis of impulsive disorders is related with earlier interruption of digital monitoring over the first 3 months. Hence, digital monitoring should be adapted for patients with impulsive disorders to increase their participation and improve their outcome.},

keywords = {Ambulatory psychiatric care, Digital monitoring, Feasibility, Naturalistic study},

pubstate = {published},

tppubtype = {article}

}

@article{10714019,

title = {Alzheimer's Disease Detection in EEG Sleep Signals},

author = {Lorena Gallego-Vi\~{n}ar\'{a}s and Juan Miguel Mira-Tom\'{a}s and Anna Michela Gaeta and Gerard Pinol-Ripoll and Ferran Barb\'{e} and Pablo M. Olmos and Arrate Mu\~{n}oz-Barrutia},

doi = {10.1109/JBHI.2024.3478380},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {29},

number = {2},

pages = {948-959},

keywords = {Sleep;Electroencephalography;Brain modeling;Databases;Data models;Predictive models;Band-pass filters;Alzheimer\'s disease;Electrodes;Recording;Alzheimer\'s disease (AD);Deep Learning (DL);Electroencephalogram (EEG);Mild Cognitive Impairment (MCI);polysomnography (PSG);semi-supervised models},

pubstate = {published},

tppubtype = {article}

}

@article{Lancho_2025,

title = {RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge},

author = {Alejandro Lancho and Amir Weiss and Gary C. F. Lee and Tejas Jayashankar and Binoy G. Kurien and Yury Polyanskiy and Gregory W. Wornell},

url = {http://dx.doi.org/10.1109/OJCOMS.2025.3556319},

doi = {10.1109/ojcoms.2025.3556319},

issn = {2644-125X},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Open Journal of the Communications Society},

volume = {6},

pages = {4083\textendash4100},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Paz-ArbaizarLopez-CastromanArtes-Rodriguez-2025-Emotionforecastingtransformer-basedapproach,

title = {Emotion forecasting: A transformer-based approach},

author = {L. Paz-Arbaizar and Jorge L\'{o}pez-Castrom\'{a}n and A. Art\'{e}s-Rodr\'{i}guez and Pablo M Olmos and D. Ram\'{i}rez},

doi = {10.2196/63962},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {J. Med. Internet Res.},

volume = {27},

pages = {e63962},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ram\'{i}rezDavid2024PDoa,

title = {Passive Detection of a Random Signal Common to Multi-Sensor Reference and Surveillance Arrays},

author = {David Ram\'{i}rez and Ignacio Santamaria and Louis L Scharf},

issn = {0018-9545},

year  = {2024},

date = {2024-01-01},

journal = {IEEE transactions on vehicular technology.},

volume = {73},

number = {7},

pages = {10106\textendash10117},

publisher = {Institute of Electrical and Electronics Engineers},

address = {New York :},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{MiguezJoaqu\'{i}n2024Mcsf,

title = {Master-slave coupling scheme for synchronization and parameter estimation in the generalized Kuramoto-Sivashinsky equation},

author = {Joaqu\'{i}n Miguez and Harold Molina-Bulla and In\'{e}s P Mari\~{n}o},

issn = {2470-0045},

year  = {2024},

date = {2024-01-01},

journal = {Physical review special topics. PRST-AB.},

volume = {110},

number = {5},

pages = {054206},

publisher = {American Physical Society,},

address = {Ridge, N.Y. :},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lopez-Castroman_Art\'{e}s-Rodr\'{i}guez_Courtet_Hanon_Gondek_Baca-Garc\'{i}a_Volpe_2024,

title = {The growing need to integrate digital mental health into psychiatric and medical education},

author = {Jorge Lopez-Castroman and Antonio Art\'{e}s-Rodr\'{i}guez and Philippe Courtet and Cecile Hanon and Tomasz Gondek and Enrique Baca-Garc\'{i}a and Umberto Volpe},

doi = {10.1192/j.eurpsy.2024.1802},

year  = {2024},

date = {2024-01-01},

journal = {European Psychiatry},

volume = {67},

number = {1},

pages = {e90},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.3389/fimmu.2024.1396284,

title = {Identification of predictive models including polymorphisms in cytokines genes and clinical variables associated with post-transplant complications after identical HLA-allogeneic stem cell transplantation},

author = {Paula Mu\~{n}iz and Mar\'{i}a Mart\'{i}nez-Garc\'{i}a and Rebeca Bail\'{e}n and Mar\'{i}a Chicano and Gillen Oarbeascoa and Juan Carlos Trivi\~{n}o and Ismael Iglesia-San Sebastian and Sara Fern\'{a}ndez de C\'{o}rdoba and Javier Anguita and Mi Kwon and Jos\'{e} Luis D\'{i}ez-Mart\'{i}n and Pablo M. Olmos and Carolina Mart\'{i}nez-Laperche and Ismael Bu\~{n}o},

url = {https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1396284},

doi = {10.3389/fimmu.2024.1396284},

issn = {1664-3224},

year  = {2024},

date = {2024-01-01},

journal = {Frontiers in Immunology},

volume = {Volume 15 - 2024},

abstract = {\<sec\>\<title\>Backgrounds\</title\>\<p\>Although allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a potentially curative therapy for hematological malignancies, it can be associated with relevant post-transplant complications. Several reports have shown that polymorphisms in immune system genes are correlated with the development of post-transplant complications. Within this context, this work focuses on identifying novel polymorphisms in cytokine genes and developing predictive models to anticipate the risk of developing graft-versus-host disease (GVHD), transplantation-related mortality (TRM), relapse and overall survival (OS).\</p\>\</sec\>\<sec\>\<title\>Methods\</title\>\<p\>Our group developed a 132-cytokine gene panel which was tested in 90 patients who underwent an HLA-identical sibling-donor allo-HSCT. Bayesian logistic regression (BLR) models were used to select the most relevant variables. Based on the cut-off points selected for each model, patients were classified as being at high or low-risk for each of the post-transplant complications (aGVHD II-IV, aGVHD III-IV, cGVHD, mod-sev cGVHD, TRM, relapse and OS).\</p\>\</sec\>\<sec\>\<title\>Results\</title\>\<p\>A total of 737 polymorphisms were selected from the custom panel genes. Of these, 41 polymorphisms were included in the predictive models in 30 cytokine genes were selected (17 interleukins and 13 chemokines). Of these polymorphisms, 5 (12.2%) were located in coding regions, and 36 (87.8%) in non-coding regions. All models had a statistical significance of p\<0.0001.\</p\>\</sec\>\<sec\>\<title\>Conclusion\</title\>\<p\>Overall, genomic polymorphisms in cytokine genes make it possible to anticipate the development all complications studied following allo-HSCT and, consequently, to optimize the clinical management of patients.\</p\>\</sec\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{StantonRamirezSantamaria-2024-Multi-channelfactoranalysisIdentifiabilityand,

title = {Multi-channel factor analysis: Identifiability and asymptotics},

author = {G. Stanton and D. Ram\'{i}rez and I. Santamar\'{i}a and L. L. Scharf and H. Wang},

doi = {10.1109/TSP.2024.3427004},

issn = {1053-587X},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Trans. Signal Process.},

volume = {72},

pages = {3562\textendash3577},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{XiaoRamirezHuang-2024-One-bittargetdetectionincolocated,

title = {One-bit target detection in colocated MIMO radar with colored background noise},

author = {Y. -H. Xiao and D. Ram\'{i}rez and L. Huang and X. -P. Li and H. C. So},

doi = {10.1109/TSP.2024.3484582},

issn = {1053-587X},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Trans. Signal Process.},

volume = {72},

pages = {5274\textendash5290},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{WuHuangRamirez-2024-One-bitspectrumsensingforcognitive,

title = {One-bit spectrum sensing for cognitive radio},

author = {P. -W. Wu and L. Huang and David Ram\'{i}rez and Y. -H. Xiao and H. C. So},

doi = {10.1109/TSP.2023.3343569},

issn = {1053-587X},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {IEEE Trans. Signal Process.},

volume = {72},

pages = {549\textendash564},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{Lancho_2023,

title = {Cell-Free Massive MIMO for URLLC: A Finite-Blocklength Analysis},

author = {Alejandro Lancho and Giuseppe Durisi and Luca Sanguinetti},

url = {http://dx.doi.org/10.1109/TWC.2023.3265303},

doi = {10.1109/twc.2023.3265303},

issn = {1558-2248},

year  = {2023},

date = {2023-12-01},

journal = {IEEE Transactions on Wireless Communications},

volume = {22},

number = {12},

pages = {8723\textendash8735},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{XiaoHuangRamirez-2023-Covariancematrixrecoveryfromone-bit,

title = {Covariance matrix recovery from one-bit data with non-zero quantization thresholds: Algorithm and performance analysis},

author = {Y. -H. Xiao and L. Huang and David Ram\'{i}rez and C. Qian and H. C. So},

doi = {10.1109/TSP.2023.3325664},

issn = {1053-587X},

year  = {2023},

date = {2023-11-01},

journal = {IEEE Trans. Signal Process.},

volume = {71},

pages = {4060\textendash4076},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{Kislal_2023,

title = {Efficient Evaluation of the Error Probability for Pilot-Assisted URLLC With Massive MIMO},

author = {A. Oguz Kislal and Alejandro Lancho and Giuseppe Durisi and Erik G. Str\"{o}m},

url = {http://dx.doi.org/10.1109/JSAC.2023.3280972},

doi = {10.1109/jsac.2023.3280972},

issn = {1558-0008},

year  = {2023},

date = {2023-07-01},

journal = {IEEE Journal on Selected Areas in Communications},

volume = {41},

number = {7},

pages = {1969\textendash1981},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{10081365,

title = {A Resource Allocation Algorithm for Collaborative Networks Using Inferred Information},

author = {David Garrido and Mai Zhang and Borja Peleato},

doi = {10.1109/ACCESS.2023.3262116},

year  = {2023},

date = {2023-01-01},

journal = {IEEE Access},

volume = {11},

pages = {34685-34697},

keywords = {Peer-to-peer computing;Interference;Receivers;Collaboration;Time-frequency analysis;Signal to noise ratio;Wireless networks;Collaborative networks;branch and bound;resource allocation;power and rate control;link scheduling;non-convex optimization;wireless networks},

pubstate = {published},

tppubtype = {article}

}