Multimodal machine-learning discrimination of recent preadmission suicidal behavior in adolescents hospitalized with depressive episodes.

BACKGROUND: Objective sleep and autonomic measures may add information beyond clinical characteristics when distinguishing adolescents with recent suicidal behavior. We evaluated multimodal machine-learning models for concurrent discrimination of recent preadmission suicidal behavior in adolescents with depressive episodes. METHODS: This retrospective cross-sectional study included 262 inpatients aged 11-17 years: 59 with actual and 27 with interrupted attempts within 3 months before admission. Stage 1 characterized 45 candidates using seven selectors; Stage 2 compared fixed predictor sets across six algorithms using five-fold nested cross-validation repeated 10 times. Additional analyses evaluated end-to-end feature and algorithm selection within outer training folds and an actual-attempt-only outcome. Mean repeat ROC-AUC was the primary measure. RESULTS: The nine-predictor XGBoost model achieved ROC-AUC 0.767 (SD, 0.012), versus 0.612 for clinical logistic regression, with PR-AUC 0.662 and Brier score 0.173. End-to-end ROC-AUC was 0.695 (SD, 0.024), versus 0.601 for matched clinical logistic regression. Actual-attempt-only ROC-AUCs were 0.751 for fixed-nine XGBoost and 0.678 for the end-to-end process. Adding inflammatory and metabolic variables provided only a small, uncertain gain in the original analysis. LIMITATIONS: This single-center, retrospective cross-sectional study used same-cohort data-informed feature-set development; additional end-to-end internal validation showed lower discrimination, and external validation remains unavailable. CONCLUSIONS: Clinical, sleep, and nocturnal autonomic features provided complementary information for concurrent discrimination of recent preadmission suicidal behavior. External and prospective validation is required before clinical use.