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Machine learning in suicide science: Applications and ethics
Author(s) -
Linthicum Kathryn P.,
Schafer Katherine Musacchio,
Ribeiro Jessica D.
Publication year - 2019
Publication title -
behavioral sciences and the law
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.649
H-Index - 74
eISSN - 1099-0798
pISSN - 0735-3936
DOI - 10.1002/bsl.2392
Subject(s) - machine learning , suicide prevention , poison control , human factors and ergonomics , scalability , artificial intelligence , computer science , suicidal behavior , data science , psychology , medicine , medical emergency , database
For decades, our ability to predict suicide has remained at near‐chance levels. Machine learning has recently emerged as a promising tool for advancing suicide science, particularly in the domain of suicide prediction. The present review provides an introduction to machine learning and its potential application to open questions in suicide research. Although only a few studies have implemented machine learning for suicide prediction, results to date indicate considerable improvement in accuracy and positive predictive value. Potential barriers to algorithm integration into clinical practice are discussed, as well as attendant ethical issues. Overall, machine learning approaches hold promise for accurate, scalable, and effective suicide risk detection; however, many critical questions and issues remain unexplored.