Abstract
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.
Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach
