VAP风险指数:使用机器学习进行呼吸机相关性肺炎的早期预测和医院表型化
Ali Samadani1, Taiyao Wang1, Kees van Zon1
1Philips Research North America, Cambridge, MA, USA.
Artificial intelligence in medicine
|December 2, 2023
概括
我们开发了一种机器学习模型,可以提前24小时预测呼吸机相关肺炎 (VAP). 在重症监护室 (ICU) 早期检测VAP有助于及时干预并改善患者的治疗结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临界护理医学 临界护理医学
背景情况:
- 呼吸机相关肺炎 (VAP) 是重症监护室 (ICU) 中疾病和死亡的重要原因.
- 早期识别VAP风险对于及时干预和改善患者结果至关重要.
- 机器学习为个性化VAP风险评估提供了一个有希望的方法.
研究的目的:
- 开发和验证用于早期检测VAP的预测模型.
- 评估模型在预测VAP风险方面24小时前的性能.
- 探索VAP预测中的医院特定变异.
主要方法:
- 使用了飞利浦eRI数据集,这是一个多机构电子医疗记录 (EMR) 数据库.
- 基于抗生素治疗和微生物学测试,对疑似VAP事件的定义标准.
- 开发了一组使用患者人口统计,生命体,实验室和呼吸机设置来预测VAP风险的决策树.
主要成果:
- 该模型实现了76%的AUC和75%的AUPRC,可以提前24小时预测VAP.
- 根据教学状态和医疗保健过程,在VAP预测中确定了医院间的差异.
- 在VAP预测中展示了队列特定的变化,包括年龄,性别和ICU死亡率状态.
结论:
- 拟议的临床标准允许开发用于早期VAP检测的模型.
- 机器学习模型在临床怀疑之前预测即将发生的VAP事件.
- 集群方法可以为不同类型的医院量身定制VAP预测模型,促进早期干预.
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