将临床编码转化为实体链接任务，提升可解释性与协作效率
Explainable ICD Coding via Entity Linking
- 把编码问题转为实体链接，每条代码附带文本证据
- 利用参数高效微调+约束解码，在少样本下表现良好
- 适合需要可解释性的医疗编码系统与临床人员
临床编码是医疗领域关键任务，但传统自动化方法难以提供足够的显式证据，而医疗编码员在实际工作中必须确保每条代码都有输入病历中明确对应的语句支持。为此，我们提出将该任务重新定义为实体链接问题：每个文档不仅标注代码，还标注对应代码的原文证据，从而促进人机协同。通过采用大语言模型的参数高效微调结合约束解码，我们提出了三种有效方法，能准确消歧临床术语，并在少样本场景下表现优异。
原文摘要 · Abstract (English)
Clinical coding is a critical task in healthcare, although traditional methods for automating clinical coding may not provide sufficient explicit evidence for coders in production environments. This evidence is crucial, as medical coders have to make sure there exists at least one explicit passage in the input health record that justifies the attribution of a code. We therefore propose to reframe the task as an entity linking problem, in which each document is annotated with its set of codes and respective textual evidence, enabling better human-machine collaboration. By leveraging parameter-efficient fine-tuning of Large Language Models (LLMs), together with constrained decoding, we introduce three approaches to solve this problem that prove effective at disambiguating clinical mentions and that perform well in few-shot scenarios.
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