CARE-AD:使用纵向临床笔记进行阿尔茨海默病预测的多因素大型语言模型框架
Rumeng Li1,2, Xun Wang3, Dan Berlowitz2,4,5
1Manning College of Information & Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA.
NPJ digital medicine
|August 23, 2025
概括
一个新的多代理大型语言模型 (LLM) 框架,CARE-AD,显示出使用电子健康记录预测阿尔茨海默病 (AD) 的前景. 与单一模型方法相比,这种方法提高了早期AD风险评估的准确性.
科学领域:
- 医学的人工智能
- 计算神经科学
- 临床信息学
背景情况:
- 大型语言模型 (LLM) 在各个领域都有潜力,但在复杂的临床预测中应用有限.
- 早期预测阿尔茨海默病 (AD) 对于及时干预和管理至关重要.
研究的目的:
- 推出CARE-AD,一个用于预测阿尔茨海默病发病的新型多因素LLM框架.
- 评估CARE-AD在分析早期AD风险评估的电子健康记录 (EHR) 的有效性.
主要方法:
- 开发了CARE-AD,该框架使用专门的LLM代理来从EHR笔记中提取AD相关的迹象和症状.
- 通过将特定领域的评估任务分配给个人LLM代理来模拟协作诊断过程.
- 进行了追溯评估,将CARE-AD与基线单一模型方法进行比较.
主要成果:
- 与基线模型相比,CARE- AD在诊断前10年预测AD风险的准确性更高 (0. 53).
- 多种药物系统在分析长度EHR数据以预测AD风险方面表现出卓越的表现.
结论:
- 多代理的LLM系统可用于支持早期阿尔茨海默病风险评估.
- 凯尔-AD的表现突显了将先进的人工智能纳入神经退行性疾病临床决策支持工作流程的潜力.
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