Mining free-text medical notes for suicide risk assessment

Marios Adamou, Vincenzo Lagani, Grigoris Antoniou, Paulos Charonyktakis, Elissavet Greasidou, Ioannis Tsamardinos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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 advance 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.
Original languageEnglish (US)
Title of host publicationACM International Conference Proceeding Series
PublisherAssociation for Computing Machineryacmhelp@acm.org
ISBN (Print)9781450364331
DOIs
StatePublished - Jul 9 2018
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-23

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