验证就是你所需要的一切:为零射击临床编码提供大型语言模型.
IEEE journal of biomedical and health informatics
|July 28, 2025
概括
大型语言模型 (LLM) 通过验证国际疾病分类 (ICD) 代码而不是生成它们来提高临床编码的性能. 这种新的方法提高了各种医疗保健数据的概括性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 临床编码将电子健康记录 (EHR) 数据转化为结构化代码,如ICD-10用于医疗保健应用.
- 目前的深度学习模型由于数据稀缺性和可变性而难以泛化.
- 大型语言模型 (LLM) 是有前途的,但在直接生成ICD代码时,其性能不足于最佳.
研究的目的:
- 提出和评估一种新的ICD编码范式,使用LLM进行增强的代码验证.
- 调查LLM作为代码验证器与直接代码生成器的有效性.
- 评估基于LLM的临床编码系统的通用性.
主要方法:
- 开发了一个新的ICD编码范式,专注于使用LLMs进行代码验证.
- 雇佣了LLM,包括GPT-4o,以验证来自候选集的代码分配.
- 在零射击环境中对CodiEsp数据集进行了广泛的实验.
主要成果:
- 作为代码验证器,LLM比作为直接代码生成器更有效地执行.
- 在零射击条件下,GPT-4o在CodiEsp数据集上实现了最佳性能.
- 基于LLM的系统表现出与最先进的临床编码系统相当的性能.
结论:
- 实际上,LLM更适合验证临床代码,而不是直接生成它们.
- 拟议的代码验证方法提高了跨机构,语言和ICD版本的通用性.
- 基于LLM的临床编码为现有系统提供了强大的替代方案.
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