TY  - JOUR
AU  - Berrouiguet, Sofian
AU  - Billot, Romain
AU  - Larsen, Mark Erik
AU  - Lopez-Castroman, Jorge
AU  - Jaussent, Isabelle
AU  - Walter, Michel
AU  - Lenca, Philippe
AU  - Baca-García, Enrique
AU  - Courtet, Philippe
PY  - 2019
DA  - 2019/05/07
TI  - An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support
JO  - JMIR Ment Health
SP  - e9766
VL  - 6
IS  - 5
KW  - clinical decision support system
KW  - data mining
KW  - electronic health
KW  - mobile phone
KW  - prevention
KW  - suicide
KW  - suicide attempts
AB  - 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. 
SN  - 2368-7959
UR  - https://mental.jmir.org/2019/5/e9766/
UR  - https://doi.org/10.2196/mental.9766
UR  - http://www.ncbi.nlm.nih.gov/pubmed/31066693
DO  - 10.2196/mental.9766
ID  - info:doi/10.2196/mental.9766
ER  -