Machine learning in suicide science: Applications and ethics

• Published in: Behavioral sciences & the law Vol. 37; no. 3; pp. 214 - 222
• Main Authors: Linthicum, Kathryn P., Schafer, Katherine Musacchio, Ribeiro, Jessica D.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.05.2019
• Subjects: Algorithms; Artificial intelligence; Clinical medicine; Ethical dilemmas; Ethics; Machine learning; Predictions; Questions; Suicide; Suicides & suicide attempts
• ISSN: 0735-3936; 1099-0798; 1099-0798
• DOI: 10.1002/bsl.2392

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.