期刊
FRONTIERS IN DIGITAL HEALTH
卷 3, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fdgth.2021.640685
关键词
machine learning; deep learning; transfer learning; intensive care unit; electronic health records; intelligent ICU
资金
- National Institute of General Medical Sciences of the National Institutes of Health [R01 GM110240]
- National Institute of Biomedical Imaging and Bioengineering [1R21EB027344]
- NSF CAREER [1750192]
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health [K01 DK120784]
Using temporal deep learning models and deep transfer learning, this pilot study integrates traditional ICU patient EHR data with novel activity sensor measurements to predict the progression and discharge outcomes of critically ill patients in the Intelligent ICU.
Accurate prediction and monitoring of patient health in the intensive care unit can inform shared decisions regarding appropriateness of care delivery, risk-reduction strategies, and intensive care resource use. Traditionally, algorithmic solutions for patient outcome prediction rely solely on data available from electronic health records (EHR). In this pilot study, we explore the benefits of augmenting existing EHR data with novel measurements from wrist-worn activity sensors as part of a clinical environment known as the Intelligent ICU. We implemented temporal deep learning models based on two distinct sources of patient data: (1) routinely measured vital signs from electronic health records, and (2) activity data collected from wearable sensors. As a proxy for illness severity, our models predicted whether patients leaving the intensive care unit would be successfully or unsuccessfully discharged from the hospital. We overcome the challenge of small sample size in our prospective cohort by applying deep transfer learning using EHR data from a much larger cohort of traditional ICU patients. Our experiments quantify added utility of non-traditional measurements for predicting patient health, especially when applying a transfer learning procedure to small novel Intelligent ICU cohorts of critically ill patients.
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