用大模型实现多语言临床术语到ICD-10编码的自动链接。
Using LLMs for Multilingual Clinical Entity Linking to ICD-10
- 分阶段处理：先用临床词典匹配明确术语，再用GPT-4进行上下文学习补全。
- 在西班牙语数据集上类别与子类别的F1分别达0.89和0.78，希腊语为0.85。
- 适用于需跨语言统一医疗编码的医院系统与研究者。
临床实体链接是从临床文本中提取结构化信息的关键步骤，即为文本中的短语分配医学本体或分类体系中的编码。国际疾病分类第10版（ICD-10）是用于统计和保险目的的国际标准。自动为出院记录中的术语分配正确的ICD-10编码可减轻医护人员负担，并确保医院编码一致性。本文提出一种基于大语言模型（LLMs）的多语言临床术语链接方法。该方法采用分阶段流水线：首先利用临床词典匹配无歧义术语，随后对未匹配项使用GPT-4进行上下文学习以预测ICD-10编码。在多个基准数据集上表现良好：西班牙语数据集CodiEsp中类别与子类别的F1分别为0.89和0.78；希腊语数据集ElCardioCC上达到0.85 F1。
原文摘要 · Abstract (English)
The linking of clinical entities is a crucial part of extracting structured information from clinical texts. It is the process of assigning a code from a medical ontology or classification to a phrase in the text. The International Classification of Diseases - 10th revision (ICD-10) is an international standard for classifying diseases for statistical and insurance purposes. Automatically assigning the correct ICD-10 code to terms in discharge summaries will simplify the work of healthcare professionals and ensure consistent coding in hospitals. Our paper proposes an approach for linking clinical terms to ICD-10 codes in different languages using Large Language Models (LLMs). The approach consists of a multistage pipeline that uses clinical dictionaries to match unambiguous terms in the text and then applies in-context learning with GPT-4.1 to predict the ICD-10 code for the terms that do not match the dictionary. Our system shows promising results in predicting ICD-10 codes on different benchmark datasets in Spanish - 0.89 F1 for categories and 0.78 F1 on subcategories on CodiEsp, and Greek - 0.85 F1 on ElCardioCC.
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