Understanding the Limits of Agentic ICD Coding
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Computer Science > Computation and Language
Title:Understanding the Limits of Agentic ICD Coding
Abstract:ICD-10-CM codes are alphanumeric codes used in the US to classify diagnoses and injuries for medical billing and epidemiological reporting. Standard ICD-10-CM benchmarks report aggregate metrics that obscure performance on complex coding scenarios. We evaluate neural, workflow, and agentic systems on a rarity-stratified set of MIMIC-IV discharge summaries and identify two orthogonal failure modes. Neural classifiers exhibit a 0.43 micro-F1 gap between rare and common codes. Workflow systems handle rare codes well but score near zero on injury and external cause codes that require multi-step guideline following. A tool-augmented agentic configuration with structured access to official ICD-10-CM reference materials recovers up to 0.34 micro-F1 on this subset. No single system dominates across all conditions.
| Comments: | Accepted to EMNLP 2026 Main Conference |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.13806 [cs.CL] |
| (or arXiv:2609.13806v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.13806
arXiv-issued DOI via DataCite (pending registration)
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