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Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial.
Critical Care Medicine ( IF 7.7 ) Pub Date : 2024-02-21 , DOI: 10.1097/ccm.0000000000006243
Matthew A Levin 1, 2, 3, 4, 5, 6 , Arash Kia 1, 4 , Prem Timsina 4 , Fu-Yuan Cheng 4 , Kim-Anh-Nhi Nguyen 4 , Roopa Kohli-Seth 5 , Hung-Mo Lin 6 , Yuxia Ouyang 1 , Robert Freeman 4 , David L Reich 1
Affiliation  

Machine learning algorithms can outperform older methods in predicting clinical deterioration, but rigorous prospective data on their real-world efficacy are limited. We hypothesized that real-time machine learning generated alerts sent directly to front-line providers would reduce escalations.

中文翻译:


实时机器学习警报以防止护理升级:一项非随机集群实用临床试验。



机器学习算法在预测临床恶化方面可以优于旧方法,但有关其现实世界疗效的严格前瞻性数据有限。我们假设实时机器学习生成的警报直接发送给一线提供商将减少升级。
更新日期:2024-02-21
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