关于开发和验证基于语言模型的大型分类器,用于识别健康的社会决定因素
Rodney A Gabriel1,2, Onkar Litake1, Sierra Simpson1
1Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA 92037.
概括
大型语言模型 (LLM) 可以有效地在临床笔记中识别健康的社会决定因素 (SDoH). 这种自动检测有助于医疗保健提供者解决健康差异并改善患者护理.
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.
- 公共卫生 公共卫生
背景情况:
- 评估健康的社会决定因素 (SDoH) 对患者护理和减少健康差异至关重要.
- 由于缺乏标准化代码,电子健康记录 (EHR) 系统难以将结构化SDoH数据纳入.
研究的目的:
- 探索大型语言模型 (LLM) 的潜力,从临床文本中提取SDoH信息.
- 开发和验证基于LLM的分类器,用于识别关键的SDoH概念.
主要方法:
- 在LLM分类器 (BERT,RoBERTa) 中,使用合成和真实的临床笔记来训练无家可归,粮食不安全和家庭暴力等SDoH概念.
- 模型在MIMIC-III和机构EHR数据集上进行了验证.
主要成果:
- 在使用组合数据集进行训练时,模型在机构数据上实现了无家可归的0.78个曲线下的面积,粮食不安全的0.72个曲线下的面积,家庭暴力的0.83个曲线下的面积.
- 在从非结构化临床文本中提取SDoH信息方面,LLM显示出显著的潜力.
结论:
- 使用LLM的自动SDoH检测可以帮助医疗保健提供者识别有风险的患者.
- 这项技术可以指导有针对性的干预措施,并支持人口健康倡议,以减轻健康差异.
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