Velupillai, S, Hadlaczky, G.., Baca-Garcia, E. et al. (8 more authors) (2019) Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior. Frontiers in Psychiatry, 10. 36. ISSN 1664-0640
Abstract
Risk assessment of suicidal behavior 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 behavior 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Velupillai, Hadlaczky, Baca-Garcia, Gorrell, Werbeloff, Nguyen, Patel, Leightley, Downs, Hotopf and Dutta. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. https://creativecommons.org/licenses/by/4.0/ |
Keywords: | suicide risk prediction; suicidality; suicide risk assessment; clinical informatics; machine learning; natural language processing |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Apr 2019 14:15 |
Last Modified: | 10 Apr 2019 01:08 |
Status: | Published |
Publisher: | Frontiers Media |
Refereed: | Yes |
Identification Number: | 10.3389/fpsyt.2019.00036 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:144680 |