@article{ref1,
title="Clustering suicides: a data-driven, exploratory machine learning approach",
journal="European psychiatry",
year="2019",
author="Ludwig, Birgit and König, Daniel and Kapusta, Nestor D. and Blüml, Victor and Dorffner, Georg and Vyssoki, Benjamin",
volume="62",
number="",
pages="15-19",
abstract="METHODS of suicide have received considerable attention in suicide research. The common approach to differentiate methods of suicide is the classification into &quot;violent&quot; versus &quot;non-violent&quot; method. Interestingly, since the proposition of this dichotomous differentiation, no further efforts have been made to question the validity of such a classification of suicides. This study aimed to challenge the traditional separation into &quot;violent&quot; and &quot;non-violent&quot; suicides by generating a cluster analysis with a data-driven, machine learning approach. In a retrospective analysis, data on all officially confirmed suicides (N = 77,894) in Austria between 1970 and 2016 were assessed. Based on a defined distance metric between distributions of suicides over age group and month of the year, a standard hierarchical clustering method was performed with the five most frequent suicide methods. In cluster analysis, poisoning emerged as distinct from all other methods - both in the entire sample as well as in the male subsample. Violent suicides could be further divided into sub-clusters: hanging, shooting, and drowning on the one hand and jumping on the other hand. In the female sample, two different clusters were revealed - hanging and drowning on the one hand and jumping, poisoning, and shooting on the other. Our data-driven results in this large epidemiological study confirmed the traditional dichotomization of suicide methods into &quot;violent&quot; and &quot;non-violent&quot; methods, but on closer inspection &quot;violent methods&quot; can be further divided into sub-clusters and a different cluster pattern could be identified for women, requiring further research to support these refined suicide phenotypes.<br><br>Copyright © 2019. Published by Elsevier Masson SAS.<p /> <p>Language: en</p>",
language="en",
issn="0924-9338",
doi="10.1016/j.eurpsy.2019.08.009",
url="http://dx.doi.org/10.1016/j.eurpsy.2019.08.009"
}