从非结构化的EHR笔记中描述痴呆症表型,使用生成性AI和可解释的机器学习
Alice S Tang1,2, Billy Z D Zeng1,3, Katherine P Rankin1,2,3
1Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA.
medRxiv : the preprint server for health sciences
|November 19, 2025
概括
大型语言模型从临床笔记中提取痴呆症症状,改善阿尔茨海默病 (AD) 和行为变异前性痴呆症 (bvFTD) 的诊断. 这种方法有助于区分这些复杂的神经疾病.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 痴呆症,包括阿尔茨海默病 (AD) 和行为变异前性痴呆症 (bvFTD),呈现出各种症状,挑战了准确的诊断.
- 电子健康记录 (EHR) 中的非结构化临床笔记包含重要的诊断信息.
- 利用高级人工智能,如大型语言模型 (LLM),可以从这些非结构化数据中获得洞察力.
研究的目的:
- 开发和验证使用LLM用于从EHR笔记中症状表型化的管道.
- 提取和结构化临床发现,以改善痴呆症综合征的特征.
- 使用提取的症状数据和机器学习来区分AD和bvFTD.
主要方法:
- 利用LLMs处理来自UCSF的超过35万个专业笔记.
- 开发了一种用于提取和结构化症状短语的管道,将其分为51个不同的症状组.
- 应用传统的机器学习 (逻辑回归) 来将患者分为AD和bvFTD综合征.
主要成果:
- 成功确定了122名bvFTD和170名AD患者的队列.
- 提取了12637个不同的症状短语,分为51个组.
- 在区分AD和bvFTD时获得了0.83的AUC,确定了关键的区分症状 (例如,bvFTD的消抑制,AD的视觉空间问题).
结论:
- 基于LLM的信息提取与机器学习相结合,为痴呆症症状表征提供了强大的方法.
- 这种方法对提高诊断准确性和开发痴呆症预测模型具有前景.
- 潜在的应用包括优化治疗策略和改善痴呆症综合征患者护理.
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