MedDCR: Learning to Design Agentic Workflows for Medical Coding
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908659493109760 |
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| author | Zheng, Jiyang Nassar, Islam Vu, Thanh Zhong, Xu Lin, Yang Liu, Tongliang Duong, Long Li, Yuan-Fang |
| author_facet | Zheng, Jiyang Nassar, Islam Vu, Thanh Zhong, Xu Lin, Yang Liu, Tongliang Duong, Long Li, Yuan-Fang |
| contents | Medical coding converts free-text clinical notes into standardized diagnostic and procedural codes, which are essential for billing, hospital operations, and medical research. Unlike ordinary text classification, it requires multi-step reasoning: extracting diagnostic concepts, applying guideline constraints, mapping to hierarchical codebooks, and ensuring cross-document consistency. Recent advances leverage agentic LLMs, but most rely on rigid, manually crafted workflows that fail to capture the nuance and variability of real-world documentation, leaving open the question of how to systematically learn effective workflows. We present MedDCR, a closed-loop framework that treats workflow design as a learning problem. A Designer proposes workflows, a Coder executes them, and a Reflector evaluates predictions and provides constructive feedback, while a memory archive preserves prior designs for reuse and iterative refinement. On benchmark datasets, MedDCR outperforms state-of-the-art baselines and produces interpretable, adaptable workflows that better reflect real coding practice, improving both the reliability and trustworthiness of automated systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13361 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MedDCR: Learning to Design Agentic Workflows for Medical Coding Zheng, Jiyang Nassar, Islam Vu, Thanh Zhong, Xu Lin, Yang Liu, Tongliang Duong, Long Li, Yuan-Fang Artificial Intelligence Multiagent Systems Medical coding converts free-text clinical notes into standardized diagnostic and procedural codes, which are essential for billing, hospital operations, and medical research. Unlike ordinary text classification, it requires multi-step reasoning: extracting diagnostic concepts, applying guideline constraints, mapping to hierarchical codebooks, and ensuring cross-document consistency. Recent advances leverage agentic LLMs, but most rely on rigid, manually crafted workflows that fail to capture the nuance and variability of real-world documentation, leaving open the question of how to systematically learn effective workflows. We present MedDCR, a closed-loop framework that treats workflow design as a learning problem. A Designer proposes workflows, a Coder executes them, and a Reflector evaluates predictions and provides constructive feedback, while a memory archive preserves prior designs for reuse and iterative refinement. On benchmark datasets, MedDCR outperforms state-of-the-art baselines and produces interpretable, adaptable workflows that better reflect real coding practice, improving both the reliability and trustworthiness of automated systems. |
| title | MedDCR: Learning to Design Agentic Workflows for Medical Coding |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2511.13361 |