CURA: Clinical Uncertainty Risk Alignment for Language Model-Based Risk Prediction
Clinical language models (LMs) are increasingly applied to support clinical risk prediction from free-text notes, yet their uncertainty estimates often remain poorly calibrated and clinically unreliable. In this work, we propose Clinical Uncertainty Risk Alignment (CURA), a framework that aligns clinical LM-based risk estimates and uncertainty with both individual error likelihoods and cohort-level ambiguities. CURA first fine-tunes domain-specific clinical LMs to obtain task-adapted patient embeddings, and then performs uncertainty fine-tuning of a multi-head classifier using a bi-level uncertainty objective. Specifically, an individual-level calibration term aligns predictive uncertaint...
| Authors | Sizhe Wang, Ziqi Xu, Claire Najjuuko et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 April 2026 |
| Last updated at source | 23 April 2026 |
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