用大模型展开医学缩写，提升病历自动编码准确率
ACE-ICD: Acronym Expansion As Data Augmentation For Automated ICD Coding
- 用大模型将临床文本中的医学缩写展开为全称，扩充训练数据
- 在MIMIC-III数据集上，对常见和罕见编码均显著提升效果
- 适合需要提升医疗文本自动化标注精度的研究者和开发者
自动ICD编码任务是为电子病历分配疾病和手术代码，对临床记录和计费至关重要。现有方法多关注代码层级关系和同义词理解，却忽视了临床笔记中广泛使用的医学缩写，而缩写正是推断编码的关键线索。为此，我们提出一种新颖有效的数据增强技术——ACE-ICD，利用大语言模型将医学缩写展开为完整形式，使模型能在全称表示下进行训练。同时引入一致性训练，强制原始文档与扩增后文档的预测结果保持一致。在MIMIC-III数据集上的大量实验表明，该方法在多种设置下（包括常见码、罕见码及全码赋值）均达到新的最优性能。代码已公开。
原文摘要 · Abstract (English)
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.
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