机器学习模型的开发,部署和持续监测,以预测重症患者的呼吸衰竭
Jonathan Y Lam1, Xiaolei Lu1, Supreeth P Shashikumar1
1Department of Biomedical Informatics, University of California San Diego, La Jolla, CA 92093, United States.
JAMIA open
|December 12, 2024
概括
一个新的机器学习 (ML) 模型,Vent.io,准确地预测了重症监护室 (ICU) 患者机械呼吸 (MV) 的需求. 它的性能被前性和外部数据验证,证明了可概括性和改善患者护理的潜力.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 机器学习用于医疗保健
背景情况:
- 预测机械通风 (MV) 对于ICU患者管理和资源分配至关重要.
- 预测MV的现有方法可能缺乏准确性或通用性.
- 机器学习 (ML) 提供了一种有前途的方法来提高重症监护中的预测能力.
研究的目的:
- 开发和部署一个新的ML模型,Vent.io,用于预测成年ICU患者机械通风 (MV) 的需要.
- 评估Vent.io模型的现实世界性能和通用性.
- 建立一个框架,用于在临床环境中持续监测和调整ML模型.
主要方法:
- 使用成年ICU患者的电子健康记录 (EHR) 数据训练了Vent.io ML模型.
- 未来将Vent.io部署在实时平台上,并评估其性能.
- 使用预先确定的更改控制计划 (PCCP) 进行基于AUC值的持续模型监测和微调.
- 在MIMIC-IV数据集上验证模型以评估可通用性.
主要成果:
- 在交叉验证过程中,Vent.io 实现了0.897 的中位数 AUC,在预期部署过程中达到0.908.
- 该模型在交叉验证中表现出0.81的特异性和0.174的PPV,敏感度为0.6.
- 在MIMIC-IV数据集上,Vent.io的AUC为0.73,引发了微调,导致AUC提高到0.873.
结论:
- Vent.io ML模型显示了强大的机械通风 (MV) 需求的预测性能.
- 一个PCCP对于保持模型通用性和适应未来部署中的数据转移至关重要.
- Vent.io有潜力提高患者护理,并优化对需要MV的重症患者的资源配置.
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