CPTCoder: A Reliable LLM System for Medical Procedure Code Prediction
Benlu Wang, Ziyao Shangguan Yale University, Kyle Tegtmeyer, Zhenyu Zhang, Sophie Chheang, Arman Cohan Yale University and Allen Institute for Artificial Intelligence
We present CPTCoder, a human-in-the-loop system that predicts standardized medical procedure codes from clinical text. Clinical procedure coding is an extreme multi-label classification problem over a long-tailed space of short numeric identifiers, where a single-digit difference denotes an entirely different procedure. CPTCoder adapts an instruction-tuned LLM with a code-aware vocabulary and constrained decoding that guarantees all outputs are valid codes. To support human review, we derive per-code posterior inclusion probabilities from n-best reweighting, producing interpretable confidence scores that rank predictions and flag uncertain cases. A post-decoding constraint repair step enforces mutual-exclusion rules between conflicting codes. To enable reproducible research in this underexplored setting, we release MIMIC-CPT, a PhysioNet-accessible benchmark of 37,885 expert-cleaned report–code pairs with a deliberately hardened test split: 88% of test examples contain label combinations unseen during training, and over a third include codes with five or fewer training occurrences. We additionally provide 413,085 weakly aligned pairs and evaluate on a separate live dataset from a hospital, which includes out-of-domain radiology reports with billing-expert-verified labels. CPTCoder achieves 0.61 and 0.51 micro-F1 on the hardened MIMIC split and Hospital-298 respectively, outperforming the strongest baseline by 12 and 5 absolute points while reducing digit-level near-miss errors.
尚无 AI 解读，展示原文摘要
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表