Invited Commentary: New Directions in Machine Learning Analyses of Administrative Data to Prevent Suicide-Related Behaviors. Issue 12 (20th April 2021)

• Record Type: Journal Article
• Authors: Bossarte, Robert M; Kennedy, Chris J; Luedtke, Alex; Nock, Matthew K; Smoller, Jordan W; Stokes, Cara; Kessler, Ronald C
• Is Part Of: American journal of epidemiology. Volume 190:Issue 12(2021)
• Page Start: 2528
• Page End: 2533
• Publication Date: 2021-04-20
• Subjects: machine learning -- prediction -- suicide; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwab111
• Languages: English
• ISSNs: 0002-9262

Abstract

Abstract: This issue contains a thoughtful report by Gradus et al. ( Am J Epidemiol . 2021;190(12):2517–2527) on a machine learning analysis of administrative variables to predict suicide attempts over 2 decades throughout Denmark. This is one of numerous recent studies that document strong concentration of risk of suicide-related behaviors among patients with high scores on machine learning models. The clear exposition of Gradus et al. provides an opportunity to review major challenges in developing, interpreting, and using such models: defining appropriate controls and time horizons, selecting comprehensive predictors, dealing with imbalanced outcomes, choosing classifiers, tuning hyperparameters, evaluating predictor variable importance, and evaluating operating characteristics. We close by calling for machine-learning research into suicide-related behaviors to move beyond merely demonstrating significant prediction—this is by now well-established—and to focus instead on using such models to target specific preventive interventions and to develop individualized treatment rules that can be used to help guide clinical decisions to address the growing problems of suicide attempts, suicide deaths, and other injuries and deaths in the same spectrum.