@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@article{Lopez-Castroman2011,

title = {Distinguishing the Relevant Features of Frequent Suicide Attempters},

author = {Jorge Lopez-Castroman and Mercedes M Perez-Rodriguez and Isabelle Jaussent and AnaLucia A Alegria and Antonio Art\'{e}s-Rodr\'{i}guez and Peter Freed and S\'{e}bastien Guillaume and Fabrice Jollant and Jose M Leiva-Murillo and Alain Malafosse and Maria A Oquendo and Mario de Prado-Cumplido and Jeronimo Saiz-Ruiz and Enrique Baca-Garc\'{i}a and Philippe Courtet},

url = {http://www.tsc.uc3m.es/~antonio/papers/P39_2011_Distinguishing the Relevant Features of Frequent Suicide Attempters.pdf 

http://www.ncbi.nlm.nih.gov/pubmed/21055768},

issn = {1879-1379},

year  = {2011},

date = {2011-01-01},

journal = {Journal of psychiatric research},

volume = {45},

number = {5},

pages = {619--625},

abstract = {BACKGROUND: In spite of the high prevalence of suicide behaviours and the magnitude of the resultant burden, little is known about why individuals reattempt. We aim to investigate the relationships between clinical risk factors and the repetition of suicidal attempts. METHODS: 1349 suicide attempters were consecutively recruited in the Emergency Room (ER) of two academic hospitals in France and Spain. Patients were extensively assessed and demographic and clinical data obtained. Data mining was used to determine the minimal number of variables that blinded the rest in relation to the number of suicide attempts. Using this set, a probabilistic graph ranking relationships with the target variable was constructed. RESULTS: The most common diagnoses among suicide attempters were affective disorders, followed by anxiety disorders. Risk of frequent suicide attempt was highest among middle-aged subjects, and diminished progressively with advancing age of onset at first attempt. Anxiety disorders significantly increased the risk of presenting frequent suicide attempts. Pathway analysis also indicated that frequent suicide attempts were linked to greater odds for alcohol and substance abuse disorders and more intensive treatment. CONCLUSIONS: Novel statistical methods found several clinical features that were associated with a history of frequent suicide attempts. The identified pathways may promote new hypothesis-driven studies of suicide attempts and preventive strategies.},

keywords = {Adult, Attempted, Attempted: psychology, Attempted: statistics \& numerical data, Female, France, Humans, Interview, Male, Middle Aged, Prevalence, Probability, Psychiatric Status Rating Scales, Psychological, Risk Factors, ROC Curve, Spain, Suicide},

pubstate = {published},

tppubtype = {article}

}