使用大型语言模型从预防性医疗指南中提取评估标准和医疗服务的零射击学习
Xiao Luo1,2, Fattah Muhammad Tahabi1, Tressica Marc3
1Department of Management Science and Information Systems, Spears School of Business, Oklahoma State University, Stillwater, OK 74078, United States.
概括
大型语言模型 (LLM) 可以自动提取电子健康记录 (EHR) 的预防护理指南. 这项研究显示了LLMs.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 将预防护理指南整合到电子健康记录 (EHR) 中,对于改善患者的治疗结果至关重要.
- 个性化预防护理建议需要有效处理复杂的临床指南.
- 自动化从指南中提取信息是一个关键的挑战.
研究的目的:
- 调查使用大型语言模型 (LLM) 来自动提取预防护理指南中的评估标准和风险因素的可行性.
- 评估最先进的LLM在处理美国预防服务特别工作组 (USPSTF) 成人指南方面的表现.
- 评估将LLM提取的数据集成到EHR系统中的潜力,以加强预防性护理的提供.
主要方法:
- 标注的标准,风险因素和预防性医疗服务来自24个USPSTF成人指南.
- 评估了三种最先进的LLM,用于从这些指南中自动提取信息.
- 开发了一个公开可用的数据结构,用于存储注释的预防指南.
主要成果:
- 在24个指导方针中,LLMs成功地自动从9个指导方针中提取了所有标准,风险因素和医疗服务.
- 所有评估的LLM在提取人口统计标准和风险因素方面都表现出强的表现.
- 在提取健康,家族史和咨询服务的社会决定因素方面,LLM的表现各不相同.
结论:
- 法律医疗管理系统在自动化提取预防护理指南信息以整合EHR方面表现有前途.
- 挑战仍然存在,包括输入令牌限制,内容生成趋势和严格验证的需要.
- 伦理考虑和医疗保健专业人员的仔细验证对于现实世界的临床应用至关重要.
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