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Clustering suicides: A data-driven, exploratory machine learning approach
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DOI：10.1016/j.eurpsy.2019.08.009.png)
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Abstract
En 中文
Methods of suicide have received considerable attention in suicide research. The common approach to differentiate methods of suicide is the classification into violent versus non-violent 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 violent and non-violent 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 violent and non-violent methods, but on closer inspection violent methods 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. (C) 2019 Published by Elsevier Masson SAS.
Keywords:
Suicide
Suicide methods
Machine-learning
Violent suicide
Cluster analysis
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