HIV Suspicion Identification

Risk-Aware Hybrid Selective Classification for HIV Suspicion Identification in Spanish Clinical Notes

This paper proposes a hybrid selective classification framework for clinical NLP that explicitly handles both aleatoric and epistemic uncertainty to avoid overconfident predictions in medical triage settings. The system combines Mondrian conformal prediction with a Multi-Centroid Mahalanobis Distance veto, evaluated on HIV suspicion identification in Spanish clinical notes. The authors demonstrate that standard uncertainty metrics and baseline classifiers suffer coverage collapse under strict reliability constraints, while their dual-verification approach isolates a trustworthy operational domain. The work critiques inflated benchmark metrics that arise from forcing deterministic classification on inherently ambiguous clinical instances.