@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 Díaz-Oliván and Antonio Artés-Rodríguez and Sofian Berrouiguet and Jorge Lopez-Castroman and Philippe Courtet and Maria Luisa Barrigón and María A Oquendo and Enrique Baca-García},
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 María Luisa Barrigón and Jorge López-Castromán and Philippe Courtet and Antonio Artés-Rodríguez and Enrique Baca-García },
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 María Luisa Barrigón and Philippe Courtet and Enrique Baca-García and Antonio Artes-Rodríguez},
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 Artés-Rodríguez and Peter Freed and Sébastien 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-García 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}
}