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通过对特定领域进行微调的大型语言模型来提高医疗编码效率

Zhen Hou¹, Hao Liu^1,2, Jiang Bian^1,3,4,5

¹Department of Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, IN USA.

npj health systems

|May 5, 2025

概括

微调大型语言模型 (LLM) 与专门的ICD-10知识显著提高了自动化医疗编码的准确性. 特定领域的LLM减少了人工负担,提高了医疗保健操作的可靠性.

科学领域:

人工智能的人工智能
医疗信息学医疗信息学
医疗编码自动化医疗编码自动化

背景情况:

医学编码是一个关键的医疗保健操作,但目前的手动流程效率低下,容易出现错误 (高达20%),成本高昂 (每年182亿美元).
现有的大型语言模型 (LLM) 在应用于医疗编码任务时显示出有限的准确性.
需要自动化解决方案来提高医疗代码生成的准确性和效率.

研究的目的:

评估微调LLM的有效性,并提供ICD-10专业知识,用于自动化医疗代码生成.
评估特定领域培训对医学编码的LLM绩效的影响.
为了比较不同LLM模型和培训方法的性能.

主要方法:

在LLMs中采用了双相微调方法.
第1阶段涉及使用74,260个ICD-10代码描述对进行初始微调.
第二阶段专注于加强培训,以解决临床文档中的语言和词汇变异.

主要成果:

最初的微调大大提高了精确匹配率,从不到1%提高到97%.
增强的微调进一步提高了性能,在真实世界的临床笔记上实现了69.20%的精确匹配和87.16%的类别匹配.
对专有 (GPT-4o mini) 和开源 (Llama) LLM 模型进行了评估.

结论:

针对特定领域精细调整的LLM可以有效地自动化医疗代码生成.
这种方法显著减少了手动编码的负担,并提高了医疗编码的可靠性.
这些发现支持将专门的LLM整合到医疗保健运营中,以提高效率和准确性.

关键词:

卫生服务卫生服务信息系统和信息技术信息系统和信息技术

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