Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications

Ji, S; Pan, S; Li, X; Cambria, E; Long, G; Huang, Z

Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications

Ji, S Pan, S

Li, X Cambria, E Long, G

Huang, Z

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Computational Social Systems, 2021, 8, (1), pp. 214-226
Issue Date:: 2021-02-01

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Field	Value	Language
dc.contributor.author	Ji, S
dc.contributor.author	Pan, S https://orcid.org/0000-0003-0794-527X
dc.contributor.author	Li, X
dc.contributor.author	Cambria, E
dc.contributor.author	Long, G https://orcid.org/0000-0003-3740-9515
dc.contributor.author	Huang, Z
dc.date.accessioned	2021-03-09T03:07:57Z
dc.date.available	2021-03-09T03:07:57Z
dc.date.issued	2021-02-01
dc.identifier.citation	IEEE Transactions on Computational Social Systems, 2021, 8, (1), pp. 214-226
dc.identifier.issn	2373-7476
dc.identifier.issn	2329-924X
dc.identifier.uri	http://hdl.handle.net/10453/146957
dc.description.abstract	© 2014 IEEE. Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people's life. Current suicidal ideation detection (SID) methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This article is the first survey that comprehensively introduces and discusses the methods from these categories. Domain-specific applications of SID are reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content. Several specific tasks and data sets are introduced and summarized to facilitate further research. Finally, we summarize the limitations of current work and provide an outlook of further research directions.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Computational Social Systems
dc.relation.isbasedon	10.1109/TCSS.2020.3021467
dc.rights	© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
dc.type	Journal Article
utslib.citation.volume	8
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-09T03:07:56Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	8
utslib.citation.issue	1

Abstract:

© 2014 IEEE. Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people's life. Current suicidal ideation detection (SID) methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This article is the first survey that comprehensively introduces and discusses the methods from these categories. Domain-specific applications of SID are reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content. Several specific tasks and data sets are introduced and summarized to facilitate further research. Finally, we summarize the limitations of current work and provide an outlook of further research directions.

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http://hdl.handle.net/10453/146957