Problem
Research questions and friction points this paper is trying to address.
ICD-10-CM
rare codes
complex coding scenarios
neural classifiers
workflow systems
Understanding the Limits of Agentic ICD Coding
🤖 AI Summary
研究评估了神经、工作流和代理系统在处理ICD-10-CM编码复杂场景中的表现，通过工具增强的代理配置提高了罕见代码分类性能。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
agentic configuration
rarity-stratified dataset
tool-augmented
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