DeLLiriuM:使用结构化的EHR在ICU中预测妄的大型语言模型
Miguel Contreras1,2, Sumit Kapoor3, Jiaqing Zhang4,2
1Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Research square
|August 20, 2025
概括
一个新的AI模型DeLLiriuM使用电子健康记录预测重症监护室 (ICU) 的妄想. 这种新的方法通过利用大量数据集和先进的语言模型来改善早期检测和患者的结果.
科学领域:
- 医学的人工智能
- 危急护理医学
- 临床信息学
背景情况:
- Delirium 影响高达 31% 的重症监护室 (ICU) 患者,其特征是注意力波动和认知障碍.
- 早期发现妄想对于及时干预和改善患者结果至关重要.
- 现有的人工智能 (AI) 模型用于ICU妄想预测,通常使用有限的数据集或旧的AI技术.
研究的目的:
- 介绍DeLLiriuM,一个基于大型语言模型 (LLM) 的妄预测模型.
- 为了预测痴呆风险,利用重症监护室入院前24小时的结构化电子健康记录 (EHR) 数据.
- 开发和验证一个能够从结构化EHR数据中捕获临床背景的模型.
主要方法:
- 通过将结构化EHR数据转换为非结构化文本格式,开发了基于LLM的DeLLiriuM模型.
- 在一个大型的多中心数据集上训练和验证该模型,其中包括来自三个数据库的104,303个ICU入院病例 (eICU,MIMIC-IV,UF-IDR).
- 在外部验证集上使用接收器操作特征曲线 (AUROC) 下面的面积来评估模型性能.
主要成果:
- 与外部验证组的基线模型相比,DeLLiriuM显示出更高的性能.
- 在194家医院的77543名患者中,该模型的AUROC为82. 5 (95% CI为81. 8-83. 1).
- 该LLM方法有效地从转换的EHR数据中获取临床上下文信息,提高预测准确性.
结论:
- DeLLiriuM是第一个基于LLM的狂妄预测工具,用于ICU使用结构化EHR数据.
- 该模型能够处理转换的电子病历数据,从而更好地预测妄想风险.
- 这种方法有助于在重症监护机构提前发现和治疗妄症.
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