Explainable artificial intelligence model to predict acute critical illness from electronic health records.
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ABSTRACT:
SUBMITTER: Lauritsen SM
PROVIDER: S-EPMC7395744 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
Publications
Explainable artificial intelligence model to predict acute critical illness from electronic health records.
Lauritsen Simon Meyer SM Kristensen Mads M Olsen Mathias Vassard MV Larsen Morten Skaarup MS Lauritsen Katrine Meyer KM Jørgensen Marianne Johansson MJ Lange Jeppe J Thiesson Bo B
Nature communications 20200731 1
Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these parameters, such as early warning scores (EWS). The predictive performance of EWSs yields a tradeoff between sensitivity and specificity that can lead to negative outcomes for the patient. Previous work on electronic health records (EHR) trained artifi ...[more]
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