用生成式AI自动完成医疗编码，准确率显著提升。
MedCodER: A Generative AI Assistant for Medical Coding
- 融合提取、检索与重排序的生成式框架
- 在ICD编码预测上达到0.60的微平均F1
- 适合医疗信息化与AI辅助诊断研究者
医疗编码对于标准化临床数据和沟通至关重要，但通常耗时且易出错。传统自然语言处理方法在自动化编码方面表现不佳，原因包括标签空间大、文本输入长，以及缺乏支持性证据标注来解释编码选择。近期生成式人工智能技术为解决这些问题提供了新思路。本文提出MedCodER，一种基于生成式AI的自动医疗编码框架，核心包含提取、检索与重排序三个组件。该模型在国际疾病分类（ICD）编码预测任务中取得了0.60的微平均F1分数，显著优于现有先进方法。此外，我们发布了新的数据集，包含带有疾病诊断、ICD编码及支持性文本证据的医学记录（https://doi.org/10.5281/zenodo.13308316）。消融实验表明，各组件的协同作用对性能至关重要，单独使用任一模块均导致性能下降。
原文摘要 · Abstract (English)
Medical coding is essential for standardizing clinical data and communication but is often time-consuming and prone to errors. Traditional Natural Language Processing (NLP) methods struggle with automating coding due to the large label space, lengthy text inputs, and the absence of supporting evidence annotations that justify code selection. Recent advancements in Generative Artificial Intelligence (AI) offer promising solutions to these challenges. In this work, we introduce MedCodER, a Generative AI framework for automatic medical coding that leverages extraction, retrieval, and re-ranking techniques as core components. MedCodER achieves a micro-F1 score of 0.60 on International Classification of Diseases (ICD) code prediction, significantly outperforming state-of-the-art methods. Additionally, we present a new dataset containing medical records annotated with disease diagnoses, ICD codes, and supporting evidence texts (https://doi.org/10.5281/zenodo.13308316). Ablation tests confirm that MedCodER's performance depends on the integration of each of its aforementioned components, as performance declines when these components are evaluated in isolation.
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