TY  - JOUR
PY  - 2014//
TI  - Suicide risk analysis
JO  - Studies in computational intelligence
A1  - Choo, C.
A1  - Diederich, J.
A1  - Song, I.
A1  - Ho, R.
SP  - 217
EP  - 228
VL  - 491
IS  - 
N2  - This study explores the trends and patterns in suicide risk factors using data mining techniques. Medical records of 666 suicide attempters who were admitted to a teaching hospital from January 2004 to December 2006 were studied. Data mining techniques revealed hidden patterns for repeated and single attempters, as well as suicide precipitants and risk factors. The findings have implications for further research in suicide assessment and intervention. © 2014 Springer-Verlag Berlin Heidelberg.<p /> <p>Language: en</p>
LA  - en
SN  - 1860-949X
UR  - http://dx.doi.org/10.1007/978-3-642-38550-6_12
ID  - ref1
ER  -