An approach for data mining of electronic health record data for suicide risk management: Database analysis for clinical decision support
Entidad
UAM. Departamento de PsiquiatríaEditor
JMIR PublicationsFecha de edición
2019-05-01Cita
10.2196/mental.9766
JMIR Mental Health 6. 5 (2019): e9766
ISSN
2368-7959DOI
10.2196/mental.9766Financiado por
This 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).Proyecto
Gobierno de España. PI13/02200; Gobierno de España. PI16/01852Versión del editor
https://doi.org/10.2196/mental.9766Materias
Clinical decision support system; Data mining; Electronic health; Mobile phone; Prevention; Suicide; Suicide attempts; MedicinaDerechos
© Sofian Berrouiguet, Romain Billot, Mark Erik Larsen, Jorge Lopez-Castroman, Isabelle Jaussent, Michel Walter, Philippe Lenca, Enrique Baca-García, Philippe CourtetResumen
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.
Lista de ficheros
Google Scholar:Berrouiguet, Sofian
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Billot, Romain
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Larsen, Mark Erik
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Lopez-Castroman, Jorge
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Jaussent, Isabelle
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Walter, Michel
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Lenca, Philippe
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Baca García, Enrique
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Courtet, Philippe
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