生成型人工智能和非结构化音频数据用于精确的公共卫生
James Anibal1,2, Adam Landa1, Hang Nguyen3
1Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, USA.
npj health systems
|June 5, 2025
概括
大型语言模型 (LLM) 分析了COVID-19患者的体验,以分类变体. 通过微妙的症状变化,LLM显示出早期流行变种检测的前景.
科学领域:
- 计算流行病学计算流行病学
- 医疗保健中的人工智能
- 传染病建模传染病建模
背景情况:
- 早期发现流行病变体对于公共卫生干预至关重要.
- 传统的方法依赖于基因测序,这可能是耗时的.
- 症状的微妙变化可能成为新变种的早期生物标志物.
研究的目的:
- 评估大语言模型 (LLM) 在根据患者报告的症状对COVID-19变体的分类中的有效性.
- 评估LLM生成的患者体验摘要对变异预测的有用性.
- 将基于LLM的变异分类与传统的症状数据分析进行比较.
主要方法:
- 用o1 LLM处理个人COVID-19经历的转录视频.
- 在LLM总结中,排除了非症状数据 (日期,接种疫苗,测试),以模拟早期的流行病状况.
- 在LLM总结上训练了一个神经网络,以预测"Omicron"与"Pre-Omicron"变体.
主要成果:
- 经过LLM训练的神经网络实现了接收器运行特征曲线 (AUROC) 下的面积为0.823.
- 在二进制症状数据上训练的比较模型实现了较低的AUROC0.769.
- 对患者叙述的LLM分析表明,在变异分类方面表现优越.
结论:
- LLM可以有效地识别微妙的症状转变,表明新的COVID-19变种.
- 从患者体验中获得的LLM的见解为早期的流行病监测提供了宝贵的工具.
- 这种方法突显了将LLM和音频数据整合到未来的流行病管理策略中的潜力.
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