@article{ref1,
title="Suicide risk analysis",
journal="Studies in computational intelligence",
year="2014",
author="Choo, C. and Diederich, J. and Song, I. and Ho, R.",
volume="491",
number="",
pages="217-228",
abstract="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>",
language="en",
issn="1860-949X",
doi="10.1007/978-3-642-38550-6_12",
url="http://dx.doi.org/10.1007/978-3-642-38550-6_12"
}