Effectiveness of artificial intelligence methods in personalized aggression risk prediction within inpatient psychiatric treatment settings - a systematic review

Aggression risk assessments are vital to prevent injuries and morbidities amongst patients and staff in psychiatric settings. More recent studies have harnessed artificial intelligence (AI) methods such as machine learning algorithms to determine factors associated with aggression in psychiatric tre...

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Main Authors: Tay, Jing Ling, Li, Ziqiang, Sim, Kang
Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/165215
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1652152023-03-26T15:41:43Z Effectiveness of artificial intelligence methods in personalized aggression risk prediction within inpatient psychiatric treatment settings - a systematic review Tay, Jing Ling Li, Ziqiang Sim, Kang Lee Kong Chian School of Medicine (LKCMedicine) Institute of Mental Health Yong Loo Lin School of Medicine, NUS Science::Medicine Aggression Risk Artificial Intelligence Aggression risk assessments are vital to prevent injuries and morbidities amongst patients and staff in psychiatric settings. More recent studies have harnessed artificial intelligence (AI) methods such as machine learning algorithms to determine factors associated with aggression in psychiatric treatment settings. In this review, using Cooper's five-stage review framework, we aimed to evaluate the: (1) predictive accuracy, and (2) clinical variables associated with AI-based aggression risk prediction amongst psychiatric inpatients. Databases including PubMed, Cochrane, Scopus, PsycINFO, CINAHL were searched for relevant articles until April 2022. The eight included studies were independently evaluated using critical appraisal tools for systematic review developed by Joanna Briggs Institute. Most of the studies (87.5%) examined health records in predicting aggression and reported acceptable to excellent accuracy with specific machine learning algorithms employed (area under curve range 0.75-0.87). No particular machine learning algorithm outperformed the others consistently across studies (area under curve range 0.61-0.87). Relevant factors identified with aggression related to demographic and social profile, past aggression, forensic history, other psychiatric history, psychopathology, challenging behaviors and management domains. The limited extant studies have highlighted a potential role for the use of AI methods to clarify factors associated with aggression in psychiatric inpatient treatment settings. Published version The study was funded by West Region, Institute of Mental Health. 2023-03-20T07:25:50Z 2023-03-20T07:25:50Z 2022 Journal Article Tay, J. L., Li, Z. & Sim, K. (2022). Effectiveness of artificial intelligence methods in personalized aggression risk prediction within inpatient psychiatric treatment settings - a systematic review. Journal of Personalized Medicine, 12(9), 1470-. https://dx.doi.org/10.3390/jpm12091470 2075-4426 https://hdl.handle.net/10356/165215 10.3390/jpm12091470 36143255 2-s2.0-85138660899 9 12 1470 en Journal of Personalized Medicine © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Medicine
Aggression Risk
Artificial Intelligence
spellingShingle Science::Medicine
Aggression Risk
Artificial Intelligence
Tay, Jing Ling
Li, Ziqiang
Sim, Kang
Effectiveness of artificial intelligence methods in personalized aggression risk prediction within inpatient psychiatric treatment settings - a systematic review
description Aggression risk assessments are vital to prevent injuries and morbidities amongst patients and staff in psychiatric settings. More recent studies have harnessed artificial intelligence (AI) methods such as machine learning algorithms to determine factors associated with aggression in psychiatric treatment settings. In this review, using Cooper's five-stage review framework, we aimed to evaluate the: (1) predictive accuracy, and (2) clinical variables associated with AI-based aggression risk prediction amongst psychiatric inpatients. Databases including PubMed, Cochrane, Scopus, PsycINFO, CINAHL were searched for relevant articles until April 2022. The eight included studies were independently evaluated using critical appraisal tools for systematic review developed by Joanna Briggs Institute. Most of the studies (87.5%) examined health records in predicting aggression and reported acceptable to excellent accuracy with specific machine learning algorithms employed (area under curve range 0.75-0.87). No particular machine learning algorithm outperformed the others consistently across studies (area under curve range 0.61-0.87). Relevant factors identified with aggression related to demographic and social profile, past aggression, forensic history, other psychiatric history, psychopathology, challenging behaviors and management domains. The limited extant studies have highlighted a potential role for the use of AI methods to clarify factors associated with aggression in psychiatric inpatient treatment settings.
author2 Lee Kong Chian School of Medicine (LKCMedicine)
author_facet Lee Kong Chian School of Medicine (LKCMedicine)
Tay, Jing Ling
Li, Ziqiang
Sim, Kang
format Article
author Tay, Jing Ling
Li, Ziqiang
Sim, Kang
author_sort Tay, Jing Ling
title Effectiveness of artificial intelligence methods in personalized aggression risk prediction within inpatient psychiatric treatment settings - a systematic review
title_short Effectiveness of artificial intelligence methods in personalized aggression risk prediction within inpatient psychiatric treatment settings - a systematic review
title_full Effectiveness of artificial intelligence methods in personalized aggression risk prediction within inpatient psychiatric treatment settings - a systematic review
title_fullStr Effectiveness of artificial intelligence methods in personalized aggression risk prediction within inpatient psychiatric treatment settings - a systematic review
title_full_unstemmed Effectiveness of artificial intelligence methods in personalized aggression risk prediction within inpatient psychiatric treatment settings - a systematic review
title_sort effectiveness of artificial intelligence methods in personalized aggression risk prediction within inpatient psychiatric treatment settings - a systematic review
publishDate 2023
url https://hdl.handle.net/10356/165215
_version_ 1761781831521271808