An approach for data mining of electronic health record data for suicide risk management: Database analysis for clinical decision support
EntityUAM. Departamento de Psiquiatría
10.2196/mental.9766JMIR Mental Health 6. 5 (2019): e9766
Funded byThis study received a Hospital Clinical Research Grant (PHRC 2009) from the French Health Ministry. None of the funding sources had any involvement in the study design; collection, analysis, or interpretation of data; writing of the report; or the decision to submit the paper for publication. This study was funded partially by Instituto de Salud Carlos III (ISCIII PI13/02200; PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073), and the American Foundation for Suicide Prevention (LSRG-1-005-16).
ProjectGobierno de España. PI13/02200; Gobierno de España. PI16/01852
SubjectsClinical decision support system; Data mining; Electronic health; Mobile phone; Prevention; Suicide; Suicide attempts; Medicina
Rights© Sofian Berrouiguet, Romain Billot, Mark Erik Larsen, Jorge Lopez-Castroman, Isabelle Jaussent, Michel Walter, Philippe Lenca, Enrique Baca-García, Philippe Courtet
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.
Background: In an electronic health context, combining traditional structured clinical assessment methods and routine electronic health-based data capture may be a reliable method to build a dynamic clinical decision-support system (CDSS) for suicide prevention. Objective: The aim of this study was to describe the data mining module of a Web-based CDSS and to identify suicide repetition risk in a sample of suicide attempters. Methods: We analyzed a database of 2802 suicide attempters. Clustering methods were used to identify groups of similar patients, and regression trees were applied to estimate the number of suicide attempts among these patients. Results: We identified 3 groups of patients using clustering methods. In addition, relevant risk factors explaining the number of suicide attempts were highlighted by regression trees. Conclusions: Data mining techniques can help to identify different groups of patients at risk of suicide reattempt. The findings of this study can be combined with Web-based and smartphone-based data to improve dynamic decision making for clinicians.
Google Scholar:Berrouiguet, Sofian - Billot, Romain - Larsen, Mark Erik - Lopez-Castroman, Jorge - Jaussent, Isabelle - Walter, Michel - Lenca, Philippe - Baca-García, Enrique - Courtet, Philippe
This item appears in the following Collection(s)
Showing items related by title, author, creator and subject.
Clinicians' concerns about mobile ecological momentary assessment tools designed for emerging psychiatric problems: Prospective acceptability assessment of the memind app Lemey, Christophe; Larsen, Mark Erik; Devylder, Jordan; Courtet, Philippe; Billot, Romain; Lenca, Philippe; Walter, Michel; Baca-García, Enrique; Berrouiguet, Sofian
Guillodo, Elise; Lemey, Christophe; Simonnet, Mathieu; Walter, Michel; Baca-García, Enrique; Masetti, Vincent; Moga, Sorin; Larsen, Mark; Ropars, Juliette; Berrouiguet, Sofian
Berrouiguet, Sofian; Perez-Rodriguez, Mercedes M.; Larsen, Mark; Baca-García, Enrique; Courtet, Philippe; Oquendo, María A.