Journal
ACUTE AND CRITICAL CARE
Volume 33, Issue 3, Pages 117-120Publisher
KOREAN SOC CRITICAL CARE MEDICINE
DOI: 10.4266/acc.2018.00290
Keywords
artificial intelligence; cardiac arrest; deep learning; rapid response team
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With the wider adoption of electronic health records, the rapid response team initially believed that mortalities could be significantly reduced but due to low accuracy and false alarms, the healthcare system is currently fraught with many challenges. Rule-based methods (e.g., Modified Early Warning Score) and machine learning (e.g., random forest) were proposed as a solution but not effective. In this article, we introduce the DeepEWS (Deep learning based Early Warning Score), which is based on a novel deep learning algorithm. Relative to the standard of care and current solutions in the marketplace, there is high accuracy, and in the clinical setting even when we consider the number of alarms, the accuracy levels are superior.
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