aceDevelopment and Internal Validation of an Interpretable Machine Learning Model for Identifying Past-Year Nonsuicidal Self-Injury Among Adolescents With Depression: Retrospective Study
Background Nonsuicidal self-injury (NSSI) is common among adolescents with depressive disorders and is associated with substantial clinical burden. Machine learning may assist in identifying multidimensional patterns associated with NSSI, although its incremental value beyond direct clinical assessment remains uncertain. Objective This study compares 7 supervised machine learning model variants for classifying past-year NSSI status among adolescents with depressive disorders and conducts detailed analyses of int…
In a held-out test set, a random forest classified past-year self-injury among adolescents with depression with AUROC 0.681 and showed greater net benefit than assess-all or assess-none strategies at thresholds of roughly 0.51-0.80.
The full random forest did not outperform a suicidal-ideation-only model, and routinely collected biochemical variables showed near-chance discrimination, so the model's added value beyond direct clinical assessment is doubtful.
Because this was a retrospective cross-sectional classification study, the findings do not establish prospective risk prediction, and external and prospective temporal validation is required before clinical implementation.
Evidence
- Peer-reviewedJMIR Pediatrics and Parenting2026-10-08
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Truvace Impact Record TRV-2026-1870, v1: “Development and Internal Validation of an Interpretable Machine Learning Model for Identifying Past-Year Nonsuicidal Self-Injury Among Adolescents With Depression: Retrospective Study.” Truvace, 2026-10-19. /record/TRV-2026-1870 (accessed at citation time). sha256 046194ee1b4fd72c…
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