机器学习建模用于预测整个ICU持续时间的侵入性和非侵入性通风的利用率
Emma Schwager1, Mohsen Nabian2, Xinggang Liu3
1Philips Research North America Cambridge Massachusetts USA.
Healthcare technology letters
|August 5, 2024
概括
一个机器学习模型准确地预测了重症监护室 (ICU) 病人的机械通风需求. 该工具有助于医院评估通风的必要性,并优化患者护理策略.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 关键护理医学 关键护理医学
背景情况:
- 机械通风是重症监护室 (ICU) 患者的关键干预措施.
- 预测需要通风对于及时和适当的患者管理至关重要.
- 现有的预测方法可能缺乏准确性或通用性.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测ICU患者侵入性和非侵入性机械通风的需求.
- 通过使用大型多中心数据集和外部验证集来评估模型的性能.
- 确定与机械通风需求相关的关键临床因素.
主要方法:
- 来自飞利浦eICU研究院 (ERI) 数据库 (2010-2019) 的260万名患者记录的分析.
- 随机数据分为培训 (63%),验证 (27%) 和测试 (10%) 集,附带额外的外部测试集用于概括性评估.
- 使用曲线下的面积 (AUC) 评估模型性能,通过将预测的概率与实际的通风使用进行比较.
主要成果:
- ML模型实现了高预测性能:整体通风的AUC为0.921,侵入性通风的AUC为0.937,非侵入性通风的AUC为0.827.
- 与较低通风可能性相关的已识别的因素包括高格拉斯哥昏迷分数,年轻年龄,较低的体重指数 (BMI) 和较低的二氧化碳部分压力 (PaCO2).
- 该模型在外部测试集上证明了可通用性.
结论:
- 开发的ML模型是预测ICU患者机械通风需求的强大工具.
- 该模型可以作为医院评估其通风策略的回顾性基准测试工具.
- 未来的应用可能包括对优化ICU机械通风管理的临床决策支持.
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