具有时间推理的大型语言模型用于纵向临床总结和预测
Maya Kruse1, Shiyue Hu1,2, Nicholas Derby1,2
1University of Colorado Anschutz Medical Campus.
概括
大型语言模型 (LLM) 对临床文本总结有希望,但与长期患者病史和时间推理作斗争. 提取增强生成 (RAG) 提供了一些好处,但并不能完全解决这些挑战.
科学领域:
- 人工智能的人工智能
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 显示了临床文本总结的潜力.
- 随着时间的推移,它们对长期患者轨迹和多模式数据的有效性尚未得到充分研究.
研究的目的:
- 系统地评估开源LLM,检索增强生成 (RAG) 和思维链 (CoT) 促使长文本临床总结和预测.
- 评估LLM在合成结构化和非结构化电子健康记录 (EHR) 数据方面的能力,同时保持时间连贯性.
主要方法:
- 重新设计现有任务:排放总结和诊断预测.
- 利用了两个公开可用的EHR数据集.
- 评估了具有RAG和CoT提示的最先进的开源LLMs.
主要成果:
- 长的上下文窗口可以改善数据集成,但不能持续进行临床推理.
- 在处理时间进展和预测罕见疾病方面,LLM存在局限性.
- 虽然RAG可以减少幻觉,但并不能完全克服现有的局限性.
结论:
- 这项研究解决了长期临床文本总结和时间推理与LLMs的差距.
- 建立了评估LLM在多模式临床数据和时间连贯性的基础.
- 突出了LLM在复杂的临床数据分析中的持续挑战.
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