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KMID : 1144420180330030117
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Acute and Critical Care
2018 Volume.33 No. 3 p.117 ~ p.120
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Deep Learning in the Medical Domain: Predicting Cardiac Arrest Using Deep Learning
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Lee Young-Nam
Kwon Joon-Myoung
Lee Ye-Ha
Park Hyun-Ho
Cho Hugh
Park Jin-Sik
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Abstract
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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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KEYWORD
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artificial intelligence, cardiac arrest, deep learning, rapid response team
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Listed journal information
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