A clinically applicable approach to continuous prediction of future acute kidney injury.

ABSTRACT: The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients¹. To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records^2-17 and using acute kidney injury-a common and potentially life-threatening condition¹⁸-as an exemplar. Our model was developed on a large, longitudinal dataset o

SUBMITTER: Tomasev N 

PROVIDER: S-EPMC6722431 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature