Acute Crit Care.  2018 Aug;33(3):117-120. 10.4266/acc.2018.00290.

Deep Learning in the Medical Domain: Predicting Cardiac Arrest Using Deep Learning

• Lee Y
• Kwon JM
• Lee Y
• Park H
• Cho H
• Park J

• KMID: 2436322
• DOI: http://doi.org/10.4266/acc.2018.00290

Abstract

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.

Keyword

artificial intelligence; cardiac arrest; deep learning; rapid response team

MeSH Terms

Artificial Intelligence
Delivery of Health Care
Electronic Health Records
Heart Arrest*
Learning*
Machine Learning
Mortality
Standard of Care

Figure

• 

  Figure 1. DeepEWS (Deep learning based Early Warning Score) operating on data from Electronic Medical Records. MEWS: Modified Early Warning Score; NIBP(S): non-invasive blood pressure (systolic); NIBP(D): non-invasive blood pressure (diastolic); HR: heart rate; RR: respiratory rate; SPO2: blood oxygen saturation; AVPU: alert, verbal, pain, unresponsive.

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  Figure 2. The area under the receiver operating characteristic of algorithms for detecting cardiac arrest. DeepEWS: Deep learning based Early Warning Score; MEWS: Modified Early Warning Score.

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  Figure 3. The sensitivity according to the number of alarms. The Y-axis indicates sensitivity, and the X-axis means the number of alarms per 1,000 patients in 1 hour (A: 20, B: 40, C: 110, D: 200, E: 330).

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