LAUC-RiskNet: Leakage-Aware and Interpretable Learning for Rule-Driven Mental-Health Labels

The significant contribution of this research is an audit-then-ablate framework that identifies and quantifies deterministic target leakage in behavioral mental-health datasets. LAUC-RiskNet combines interpretable threshold learning, contextual encoding, adaptive fusion, probability calibration, and uncertainty estimation. Crucially, ablation shows that the apparent predictive performance arises mainly from the label-generation rule rather than independent clinical signals, enabling more transparent and trustworthy evaluation.