通过LLM,可以对美国卫生系统中患者自我报告的症状和需求进行分类
Bilal A Naved1,2,3, Shravan Ravishankar2, Georges E Colbert2,4
1Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
NPJ digital medicine
|July 2, 2025
概括
在卫生系统网站上准确分类患者搜索至关重要. 深度神经网络分类器和GPT-4展示了有效的患者搜索分类,改善了医疗保健导航.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者参与 患者参与
背景情况:
- 美国卫生系统每月吸引多达2亿名访客.
- 准确分类患者网站搜索对于引导用户到适当的工作流程至关重要.
- 现有的搜索分类方法可能缺乏满足各种卫生系统需求所需的精度.
研究的目的:
- 开发和评估一个深度神经网络 (DNN) 分类器,用于美国卫生系统网站上的多标签,多类患者搜索术语.
- 将DNN分类器的性能与大型语言模型 (LLM) 的性能进行比较,特别是GPT-4.
- 通过迄今为止最大的全国性研究,描述美国卫生系统内的患者搜索行为.
主要方法:
- 来自大约15个美国卫生系统网站的患者搜索数据集被注释和描述.
- 在注释数据集上训练和评估了一个多标签,多类深度神经网络.
- 训练有素的分类器在全国范围内部署,并与GPT-4的性能进行了比较,有或没有类的主清单.
主要成果:
- 根据类数,DNN分类器在各种指标中实现了大约0.90至0.70的性能,这取决于类数.
- 当提供搜索类的主清单时,GPT-4显示了可比性能.
- 该研究收集了揭示患者搜索特征的数据,形成了美国卫生系统中最大的全国数据集.
结论:
- 一个监督的深度神经网络分类器是有效的对分类大量的病人搜索查询在医疗系统网站.
- 像GPT-4这样的大型语言模型可以增强监督分类器,提供额外的覆盖范围和性能改进.
- 这些发现为患者搜索特征提供了宝贵的见解,有助于优化数字健康导航和患者参与策略.
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