期刊
NATURE MEDICINE
卷 24, 期 11, 页码 1716-+出版社
NATURE PORTFOLIO
DOI: 10.1038/s41591-018-0213-5
关键词
-
资金
- National Institute of Health Research (NIHR) Comprehensive Biomedical Research Centre based at Imperial College Healthcare NHS Trust and Imperial College London
- Engineering and Physical Sciences Research Council
- Imperial College President's PhD Scholarship
- NIHR Research Professorship award [RP-2015-06-018]
Sepsis is the third leading cause of death worldwide and the main cause of mortality in hospitals(1-3), but the best treatment strategy remains uncertain. In particular, evidence suggests that current practices in the administration of intravenous fluids and vasopressors are suboptimal and likely induce harm in a proportion of patients(1,4-6). To tackle this sequential decision-making problem, we developed a reinforcement learning agent, the Artificial Intelligence (AI) Clinician, which extracted implicit knowledge from an amount of patient data that exceeds by many-fold the life-time experience of human clinicians and learned optimal treatment by analyzing a myriad of (mostly suboptimal) treatment decisions. We demonstrate that the value of the AI Clinician's selected treatment is on average reliably higher than human clinicians. In a large validation cohort independent of the training data, mortality was lowest in patients for whom clinicians' actual doses matched the AI decisions. Our model provides individualized and clinically interpretable treatment decisions for sepsis that could improve patient outcomes.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据