Automatic ICD coding, the task of assigning disease and procedure codes to electronic medical records, is crucial for clinical documentation and billing. While existing methods primarily enhance model understanding of code hierarchies and synonyms, they often overlook the pervasive use of medical acronyms in clinical notes, a key factor in ICD code inference. To address this gap, we propose a novel effective data augmentation technique that leverages large language models to expand medical acronyms, allowing models to be trained on their full form representations. Moreover, we incorporate consistency training to regularize predictions by enforcing agreement between the original and augmented documents. Extensive experiments on the MIMIC-III dataset demonstrate that our approach, ACE-ICD establishes new state-of-the-art performance across multiple settings, including common codes, rare codes, and full-code assignments. Our code is publicly available.

翻译：自动化ICD编码，即向电子病历分配疾病和操作代码的任务，对于临床记录和计费至关重要。虽然现有方法主要增强模型对代码层级结构和同义词的理解，但它们常常忽视临床笔记中普遍使用的医学缩略语，而这是ICD代码推断的一个关键因素。为弥补这一不足，我们提出了一种新颖有效的数据增强技术，该技术利用大型语言模型扩展医学缩略语，使模型能够在缩略语完整形式表示上进行训练。此外，我们引入一致性训练，通过强制原始文档与增强文档的预测结果一致来规范化预测。在MIMIC-III数据集上进行的大量实验表明，我们的方法ACE-ICD在多种设置下（包括常见代码、罕见代码和完整代码分配）均取得了新的最先进性能。我们的代码已公开可用。