利用专家知识和因果结构学习在儿科重症监护室 (PICU) 构建急性脑功能障碍的节模型
medRxiv : the preprint server for health sciences
|February 27, 2026
概括
将临床专业知识与因果结构学习相结合,确定了儿科重症监护室 (PICU) 中急性脑功能障碍 (ABD) 的关键预测因素. 这种方法开发了使用更少生物标志物的准确,可解释的预测模型.
科学领域:
- 生物医学信息学 生物医学信息学
- 临床决策支持 临床决策支持
- 人工智能在医学中的应用
背景情况:
- 临床决策支持系统中的机器学习 (ML) 面临着透明度和稳健性的挑战.
- 因果结构学习 (CSL) 与专家知识相结合,可以提高ML模型的解释性和临床调整.
- 在儿科重症监护室 (PICU) 获得的急性脑功能障碍 (ABD) 需要强大的预测建模.
研究的目的:
- 将临床医生的专业知识与CSL算法相结合,以确定PICU中获得的ABD的因果驱动因素.
- 为 ABD 开发节的预测模型,而不会造成显著的性能损失.
- 提高基于ML的决策支持的透明度和临床相关性.
主要方法:
- 从一家主要医疗中心 (2010-2022) 分析了18568例PICU遭遇.
- 从四名临床医生中获得专家知识,以构建共识定向非循环图 (DAG).
- 应用CSL算法 (GOLEM,PC-MB) 丰富专家衍生的DAG和XGBoost预测模型的开发.
主要成果:
- 临床医生的共识确定了16种潜在的因果生物标志物,具有可接受的评价者间可靠性 (Fleiss' Kappa = 0.62).
- PC-MB算法显示了78%的一致性与专家的共识,确定了7个额外的潜在因果生物标志物.
- 使用专家共识和PC-MB交叉点的14个生物标志物的XGBoost模型获得了0.79的AUPRC,与使用所有45个生物标志物的模型相比 (AUPRC = 0.81).
结论:
- 将临床专业知识与CSL结合起来,可以有效地识别获得ABD的可信因果驱动因素.
- 这种综合方法有助于在PICU中开发储蓄性和临床一致的ABD预测模型.
- 这项研究表明了改善临床决策支持中ML透明度和稳健性的可行策略.
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