Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior

Sumithra Velupillai, Gergö Hadlaczky, Enrique Baca-Garcia, Genevieve M. Gorrell, Nomi Werbeloff, Dong Nguyen, Rashmi Patel, Daniel Leightley, Johnny Downs, Matthew Hotopf, Rina Dutta

Research output: Contribution to journalArticlepeer-review

Abstract

Risk assessment of suicidal behaviour is a time-consuming but notoriously inaccurate activity for mental health services globally. In the last 50 years a large number of tools have been designed for suicide risk assessment, and tested in a wide variety of populations, but studies show that these tools suffer from low positive predictive values. More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift in advancing precision medicine. In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behaviour and risk. We provide a perspective on the strengths and weaknesses of these applications to mental health-related data, and suggest research directions to enable improvement in clinical practice.
Original languageEnglish
Pages (from-to)36
Number of pages1
JournalFrontiers in psychiatry
Volume10
DOIs
Publication statusPublished - 13 Feb 2019

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