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.

本研究针对医学编码中的时间消耗和错误率高的问题，提出了一种新颖的生成性人工智能框架MedCodER，利用提取、检索和再排序技术进行自动医学编码。研究显示，MedCodER在国际疾病分类（ICD）代码预测方面达到了0.60的微F1分数，显著优于现有方法，展示了其在医学编码领域的潜在影响。

MedCodER：医学编码的生成性人工智能助手