@inproceedings{wang-etal-2026-cptcoder,
    title = "{CPTC}oder: A Reliable {LLM} System for Medical Procedure Code Prediction",
    author = "Wang, Benlu  and
      Shangguan, Ziyao  and
      Tegtmeyer, Kyle  and
      Zhang, Zhenyu  and
      Chheang, Sophie  and
      Cohan, Arman",
    editor = "Durrett, Greg  and
      Jian, Ping",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-demo.60/",
    pages = "605--614",
    ISBN = "979-8-89176-392-0",
    abstract = "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."
}