MedDCR: Learning to Design Agentic Workflows for Medical Coding

• URL: http://arxiv.org/abs/2511.13361v1
• Date: Mon, 17 Nov 2025 13:30:51 GMT
• Title: MedDCR: Learning to Design Agentic Workflows for Medical Coding
• Authors: Jiyang Zheng, Islam Nassar, Thanh Vu, Xu Zhong, Yang Lin, Tongliang Liu, Long Duong, Yuan-Fang Li,
• Abstract summary: Medical coding converts free-text clinical notes into standardized diagnostic and procedural codes.<n>We present MedDCR, a closed-loop framework that treats design as a learning problem.<n>On benchmark datasets, MedDCR outperforms state-of-the-art baselines.
• Score: 55.51674334874892
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution [63.128360383691295]
  We propose MedVerse, a reasoning framework for complex medical inference.<n>For data creation, we introduce the MedVerse Curator, which synthesizes knowledge-grounded medical reasoning paths.<n>We develop a customized inference engine that supports parallel execution without additional overhead.
  arXiv  Detail & Related papers  (2026-02-07T12:54:01Z)
• DeepCode: Open Agentic Coding [11.7906174865581]
  DeepCode is a fully autonomous framework for document-to-codebase synthesis.<n>It orchestrates four information operations to maximize task-relevant signals under finite context budgets.<n>Extensive evaluations on the PaperBench benchmark demonstrate that DeepCode achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-12-08T16:07:13Z)
• Simple Agents Outperform Experts in Biomedical Imaging Workflow Optimization [69.36509281190662]
  Adapting production-level computer vision tools to bespoke scientific datasets is a critical "last mile" bottleneck.<n>We consider using AI agents to automate this manual coding, and focus on the open question of optimal agent design.<n>We demonstrate that a simple agent framework consistently generates adaptation code that outperforms human-expert solutions.
  arXiv  Detail & Related papers  (2025-12-02T18:42:26Z)
• From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence [150.3696990310269]
  Large language models (LLMs) have transformed automated software development by enabling direct translation of natural language descriptions into functional code.<n>We provide a comprehensive synthesis and practical guide (a series of analytic and probing experiments) about code LLMs.<n>We analyze the code capability of the general LLMs (GPT-4, Claude, LLaMA) and code-specialized LLMs (StarCoder, Code LLaMA, DeepSeek-Coder, and QwenCoder)
  arXiv  Detail & Related papers  (2025-11-23T17:09:34Z)
• Expert Consensus-based Video-Based Assessment Tool for Workflow Analysis in Minimally Invasive Colorectal Surgery: Development and Validation of ColoWorkflow [4.2710633896098456]
  Video-based assessment (VBA) offers an opportunity to generate data-driven insights to reduce variability, optimize training, and improve surgical performance.<n>Existing tools for workflow analysis remain difficult to standardize and implement.<n>This study aims to develop and validate a VBA tool for workflow analysis across minimally invasive colorectal procedures.
  arXiv  Detail & Related papers  (2025-11-13T19:33:36Z)
• Toward Reliable Clinical Coding with Language Models: Verification and Lightweight Adaptation [3.952186976672079]
  We show that lightweight interventions, including prompt engineering and small-scale fine-tuning, can improve accuracy without the computational overhead of search-based methods.<n>To address hierarchically near-miss errors, we introduce clinical code verification as both a standalone task and a pipeline component.
  arXiv  Detail & Related papers  (2025-10-08T23:50:58Z)
• Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning [70.04746094652653]
  We introduce PaperCoder, a framework that transforms machine learning papers into functional code repositories.<n>PaperCoder operates in three stages: planning, designs the system architecture with diagrams, identifies file dependencies, and generates configuration files.<n>We then evaluate PaperCoder on generating code implementations from machine learning papers based on both model-based and human evaluations.
  arXiv  Detail & Related papers  (2025-04-24T01:57:01Z)
• Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review [14.381199039813675]
  This position paper aims to align AI coding research more closely with practical challenges of clinical coding.<n>Based on our analysis, we offer eight specific recommendations, suggesting ways to improve current evaluation methods.
  arXiv  Detail & Related papers  (2024-12-23T23:39:05Z)
• Exploring LLM Multi-Agents for ICD Coding [15.730751450511333]
  The proposed multi-agent method for ICD coding effectively mimics the real-world coding process and improves performance on both common and rare codes. Our method achieves comparable results to state-of-the-art ICD coding methods that require extensive pre-training or fine-tuning, and outperforms them in rare code accuracy, and explainability.
  arXiv  Detail & Related papers  (2024-04-01T15:17:39Z)
• CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning [56.782963838838036]
  We propose a novel approach, a contextualized and flexible framework, to enhance the learning of ICD code representations. Our approach employs a dependent learning paradigm that considers the context of clinical notes in modeling all possible code relations.
  arXiv  Detail & Related papers  (2024-02-24T03:25:28Z)
• Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study [60.56194508762205]
  We reproduce, compare, and analyze state-of-the-art automated medical coding machine learning models. We show that several models underperform due to weak configurations, poorly sampled train-test splits, and insufficient evaluation. We present the first comprehensive results on the newly released MIMIC-IV dataset using the reproduced models.
  arXiv  Detail & Related papers  (2023-04-21T11:54:44Z)
• Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study [2.871614744079523]
  It is not clear if pretrained models are useful for medical code prediction without further architecture engineering. We propose a hierarchical fine-tuning architecture to capture interactions between distant words and adopt label-wise attention to exploit label information. Contrary to current trends, we demonstrate that a carefully trained classical CNN outperforms attention-based models on a MIMIC-III subset with frequent codes.
  arXiv  Detail & Related papers  (2021-03-11T07:23:45Z)
• A Meta-embedding-based Ensemble Approach for ICD Coding Prediction [64.42386426730695]
  International Classification of Diseases (ICD) are the de facto codes used globally for clinical coding. These codes enable healthcare providers to claim reimbursement and facilitate efficient storage and retrieval of diagnostic information. Our proposed approach enhances the performance of neural models by effectively training word vectors using routine medical data as well as external knowledge from scientific articles.
  arXiv  Detail & Related papers  (2021-02-26T17:49:58Z)
• Learning Contextualized Document Representations for Healthcare Answer Retrieval [68.02029435111193]
  Contextual Discourse Vectors (CDV) is a distributed document representation for efficient answer retrieval from long documents. Our model leverages a dual encoder architecture with hierarchical LSTM layers and multi-task training to encode the position of clinical entities and aspects alongside the document discourse. We show that our generalized model significantly outperforms several state-of-the-art baselines for healthcare passage ranking.
  arXiv  Detail & Related papers  (2020-02-03T15:47:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.