Table 1_Development and internal validation of machine learning models for identifying suicidal behavior in inpatients with mood disorders.docx

Elastic Net selected 14 predictors from 65 candidates in the development set. We compared 13 classifiers from six model families. All preprocessing and resampling were confined to training folds, and evaluation used only observed patients.

Assessment included repeated fivefold cross-validation, probability calibration, bootstrap confidence intervals, precision-recall and decision-curve analyses, clinical baselines, and sensitivity analyses.</p>Results<p>The analytic cohort comprised 952 SB-negative and 126 SB-positive patients (11.69%). The development set contained 864 patients (97 SB-positive), and the observed test set contained 214 patients (29 SB-positive).

Calibrated linear discriminant analysis (LDA) provided the most consistent balance of discrimination and calibration. In repeated cross-validation, mean AUC was 0.721 ± 0.052, mean AUPRC was 0.268 ± 0.060, and mean Brier score was 0.095. In the observed test set, AUC was 0.704 (0.597–0.803), AUPRC was 0.283 (0.198–0.453), and Brier score was 0.111.

At the training-derived threshold of 0.111, sensitivity was 0.552 and specificity was 0.659. The leading SHAP contributions were onset-polarity category, previous SA, age, and previous SB, but all attributions were associational.</p>Conclusions<p>Model performance was acceptable. These internally validated findings are exploratory and require prospective assessment, standardized outcome measurement, and external validation before clinical use.</p>