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Machine learning techniques offer promise to improve suicide risk prediction. In the current systematic review, we aimed to review the existing literature on the application of machine learning techniques to predict self-injurious thoughts and behaviors (SITBs).

We systematically searched PsycINFO, PsycARTICLES, ERIC, CINAHL, and MEDLINE for articles published through February 2018.

Thirty-five articles met criteria to be included in the review. Included articles were reviewed by outcome: suicide death, suicide attempt, suicide plan, suicidal ideation, suicide risk, and non-suicidal self-injury. We observed three general aims in the use of SITB-focused machine learning analyses: (1) improving prediction accuracy, (2) identifying important model indicators (i.e., variable selection) and indicator interactions, and (3) modeling underlying subgroups. For studies with the aim of boosting …