Cautions, Concerns, and Future Directions for Using Machine Learning in Relation to Mental Health Problems and Clinical and Forensic Risks: A Brief Comment on "Model Complexity Improves the Prediction of Nonsuicidal Self-Injury" (Fox et al., 2019).
Machine learning (ML) is an increasingly popular approach/technique for analyzing "Big Data" and predicting risk behaviors and psychological problems. However, few published critiques of ML as an approach currently exist. We discuss some fundamental cautions and concerns with ML that are relevant wh...
| Published in: | Journal of Consulting & Clinical Psychology Vol. 88; no. 4; pp. 384 - 388 |
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| Main Authors: | , , , , |
| Format: | Article |
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American Psychological Association
Apr2020
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=142066491&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 142066491 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0022006X JCC jtl: Journal of Consulting & Clinical Psychology issn: 0022006X maglogo: N pubinfo: dt: Apr2020 vid: 88 iid: 4 pid: 34 pub: American Psychological Association artinfo: ui: 142066491 10.1037/ccp0000485 ppf: 384 ppct: 4 formats: tig: atl: Cautions, Concerns, and Future Directions for Using Machine Learning in Relation to Mental Health Problems and Clinical and Forensic Risks: A Brief Comment on "Model Complexity Improves the Prediction of Nonsuicidal Self-Injury" (Fox et al., 2019). aug: au: Siddaway, Andy P. Quinlivan, Leah Kapur, Nav O'Connor, Rory C. de Beurs, Derek affil: Suicidal Behavior Research Laboratory, Institute of Health and Wellbeing, University of Glasgow Centre for Mental Health and Safety, University of Manchester Department of Clinical Psychology, Vrije Universiteit Amsterdam su: Mental health Risk-taking behavior Health behavior Machine learning sug: subj: Mental health Risk-taking behavior Health behavior Offices of Mental Health Practitioners (except Physicians) Machine learning keyword: machine learning prediction risk self-injury suicide machine learning prediction risk self-injury suicide ab: Machine learning (ML) is an increasingly popular approach/technique for analyzing "Big Data" and predicting risk behaviors and psychological problems. However, few published critiques of ML as an approach currently exist. We discuss some fundamental cautions and concerns with ML that are relevant when attempting to predict all clinical and forensic risk behaviors (risk to self, risk to others, risk from others) and mental health problems. We hope to provoke a healthy scientific debate to ensure that ML's potential is realized and to highlight issues and directions for future risk prediction, assessment, management, and prevention research. ML, by definition, does not require the model to be specified by the researcher. This is both its key strength and its key weakness. We argue that it is critical that the ML algorithm (the model or models) and the results are both presented and that ML needs to be become machine-assisted learning like other statistical techniques; otherwise, we run the risk of becoming slaves to our machines. Emerging evidence potentially challenges the superiority of ML over other approaches, and we argue that ML's complexity significantly limits its clinical utility. Based on the available evidence, we believe that researchers and clinicians should emphasize identifying, understanding, and explaining (formulating) individual clinical needs and risks and providing individualized management and treatment plans, rather than trying to predict or putting too much trust in predictions that will inevitably be wrong some of the time (and we do not know when). What is the public health significance of this article?: Machine learning is a statistical approach/technique that is increasingly being used in an attempt to improve the accuracy with which risky behaviors and mental health problems are predicted. This article discusses some key considerations for using machine learning and making it even more useful. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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