TY  - JOUR
PY  - 2019//
TI  - An approach for data mining of electronic health record data for suicide risk management: database analysis for clinical decision support
JO  - JMIR mental health
A1  - Berrouiguet, Sofian
A1  - Billot, Romain
A1  - Larsen, Mark Erik
A1  - Lopez-Castroman, Jorge
A1  - Jaussent, Isabelle
A1  - Walter, Michel
A1  - Lenca, Philippe
A1  - Baca-Garcia, Enrique
A1  - Courtet, Philippe
SP  - e9766
EP  - e9766
VL  - 6
IS  - 5
N2  - 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. <br><br>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. <br><br>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. <br><br>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. <br><br>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.<br><br>©Sofian Berrouiguet, Romain Billot, Mark Erik Larsen, Jorge Lopez-Castroman, Isabelle Jaussent, Michel Walter, Philippe Lenca, Enrique Baca-García, Philippe Courtet. Originally published in JMIR Mental Health (http://mental.jmir.org), 07.05.2019.<p />  <p>Language: en</p>
LA  - en
SN  - 2368-7959
UR  - http://dx.doi.org/10.2196/mental.9766
ID  - ref1
ER  -