首页|Clustering suicides: A data-driven, exploratory machine learning approach

Clustering suicides: A data-driven, exploratory machine learning approach

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  • NSTL
  • Elsevier
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.

SuicideSuicide methodsMachine-learningViolent suicideCluster analysis

Ludwig, Birgit、Koenig, Daniel、Kapusta, Nestor D.、Blueml, Victor、Dorffner, Georg、Vyssoki, Benjamin

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Med Univ Vienna, Dept Psychiat & Psychotherapy, Clin Div Gen Psychiat, Wahringer Gurtel 18-20, A

Med Univ Vienna, Dept Psychiat & Psychotherapy, Clin Div Social Psychiat, Wahringer Gurtel 18-20, A

Med Univ Vienna, Dept Psychoanal & Psychotherapy, Wahringer Gurtel 18-20, A-1090 Vienna, Austria

Med Univ Vienna, Ctr Med Stat Informat & Intelligent Syst, Sect Artificial Intelligence & Decis

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2019

10.1016/j.eurpsy.2019.08.009

European psychiatry :

European psychiatry :

ISSN：0924-9338
年,卷(期)：2019.62
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