Published in: SETN2018, July 2018, Patras, Greece

Authors

Ioannis Tsamardinos, Pavlos Charonyktakis, Vincenzo Lagani

Marios Adamou, Grigoris Antoniou, Elissavet Greasidou

Abstract

Suicide has been considered as an important public health issue for a very long time and is one of the main causes of death worldwide.
Despite suicide prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Advances in
machine learning make it possible to attempt to predict suicide based on the analysis of relevant data to inform clinical practice. This paper
reports on findings from the analysis of data of patients who died by suicide in the period 2013-2016 and made use of both structured data and
free-text medical notes. We focus on examining various text-mining approaches to support risk assessment. The results show that using
advanced machine learning and text-mining techniques, it is possible to predict within a specified period which people are most at risk of
taking their own life at the time of referral to a mental health service.

Keywords

automated machine learning, suicide prevention, risk assessment tool, clinical data, text mining