FHIR-GPT增强了与大型语言模型的健康互操作性
Yikuan Li1,2, Hanyin Wang1, Halid Z Yerebakan2
1Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago.
NEJM AI
|August 1, 2025
概括
大型语言模型 (LLM) 可以有效地将非结构化的临床文本转换为快速医疗互操作资源 (FHIR) 药物陈述. 这一进步显著提高了研究和公共卫生倡议的健康数据互操作性.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 医疗保健中的人工智能
背景情况:
- 健康数据的互操作性对于研究,临床试验和公共卫生监测至关重要.
- 快速医疗互操作性资源 (FHIR) 标准由联邦机构推广,以促进互操作性.
- 将异构的电子健康记录数据,特别是非结构化文本转化为FHIR资源,带来了重大挑战.
研究的目的:
- 评估大型语言模型 (LLM) 在将临床叙述转化为FHIR资源方面的能力.
- 开发和评估一个特定的LLM,FHIR-GPT,用于将临床文本转化为FHIR药物陈述.
主要方法:
- 开发FHIR-GPT,一种针对临床文本进行FHIR药物陈述转换的生成预训练型变压器模型.
- 使用3671个临床文本片段进行实验评估,以比较FHIR-GPT与现有方法的性能.
主要成果:
- 在将临床文本转化为FHIR药物陈述时,FHIR-GPT实现了超过90%的准确匹配率.
- 与现有的NLP管道相比,FHIR-GPT显著改进,特别是在提取时间表 (超过50%),表格 (42%) 和理由 (35%) 方面.
结论:
- 大型语言模型显示了通过有效地将非结构化临床叙述转换为结构化FHIR资源,提高健康数据互操作性的巨大潜力.
- FHIR-GPT提供了一个有希望的解决方案,以克服与健康数据转换相关的挑战,降低开发成本和提高准确性.
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