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一个机器学习模型用于识别入住重症监护室的患者的肺栓塞
Sampath Rapuri1, Kirby Gong2, Carl Harris1
1Department of Biomedical Engineering, Whiting School of Engineering, USA.
Computers in biology and medicine
|February 14, 2026
概括
机器学习使用常规临床数据准确地识别了重症患者的肺栓塞 (PE). 该模型的性能优于现有的风险评分,为在重症监护室 (ICU) 早期发现PE提供了有价值的工具.
科学领域:
- 关键护理医学 关键护理医学
- 医疗保健中的机器学习
- 肺栓塞的诊断 肺栓塞的诊断 肺栓塞的诊断
背景情况:
- 肺栓塞 (PE) 是可预防死亡的一个重要原因.
- 现有的PE统计预测模型已经证明了不一致的有效性.
- 在重症患者中准确识别PE仍然是一个挑战.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于在重症监护室 (ICU) 患者中识别急性PE.
- 评估在常规收集的临床数据上训练的ML模型是否可以准确检测PE.
- 将ML模型的性能与已建立的PE风险得分进行比较.
主要方法:
- 使用了两个多中心数据集 (开发和外部验证队列).
- 从人口统计学,并发病情况以及ICU入院后的生理/实验室数据中提取特征.
- 训练后勤回归ML模型并将性能 (AUROC,AUCPR) 与两个基准PE风险得分进行比较.
主要成果:
- ML模型在开发队列中实现了0.829的AUROC,在外部验证队列中达到0.819.
- 业绩明显超过了基准风险得分 (AUROC 0.704 和 0.667).
- 该模型显示出优异的区分,正如在精度回忆曲线值下的更高面积所示.
结论:
- 机器学习模型可以使用常规收集的临床数据准确地检测ICU患者的PE.
- 开发的ML模型是一个经过验证的工具,其性能优于现有的基准风险得分.
- 这种ML模型有可能成为评估重症患者PE可能性的有价值的辅助工具.
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