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Open AccessArticle

Predicting Cardiac Arrest and Respiratory Failure Using Feasible Artificial Intelligence with Simple Trajectories of Patient Data

1
Department of Anesthesiology and Pain Medicine, Severance Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Korea
2
Computer Engineering, AI R&D Lab. of Selvas AI Inc., Seoul 08594, Korea
3
Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul 03722, Korea
4
Division of Medical Information and Technology, Yonsei University Health System, Seoul 03722, Korea
5
Cardiovascular Research Institute, College of Medicine, Yonsei University, Seoul 03722, Korea
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2019, 8(9), 1336; https://doi.org/10.3390/jcm8091336
Received: 2 July 2019 / Revised: 23 August 2019 / Accepted: 26 August 2019 / Published: 29 August 2019
(This article belongs to the Special Issue The Future of Artificial Intelligence in Clinical Medicine)
We introduce a Feasible Artificial Intelligence with Simple Trajectories for Predicting Adverse Catastrophic Events (FAST-PACE) solution for preparing immediate intervention in emergency situations. FAST-PACE utilizes a concise set of collected features to construct an artificial intelligence model that predicts the onset of cardiac arrest or acute respiratory failure from 1 h to 6 h prior to its occurrence. Data from the trajectory of 29,181 patients in intensive care units of two hospitals includes periodic vital signs, a history of treatment, current health status, and recent surgery. It excludes the results of laboratory data to construct a feasible application in wards, out-hospital emergency care, emergency transport, or other clinical situations where instant medical decisions are required with restricted patient data. These results are superior to previous warning scores including the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS). The primary outcome was the feasibility of an artificial intelligence (AI) model predicting adverse events 1 h to 6 h prior to occurrence without lab data; the area under the receiver operating characteristic curve of this model was 0.886 for cardiac arrest and 0.869 for respiratory failure 6 h before occurrence. The secondary outcome was the superior prediction performance to MEWS (net reclassification improvement of 0.507 for predicting cardiac arrest and 0.341 for predicting respiratory failure) and NEWS (net reclassification improvement of 0.412 for predicting cardiac arrest and 0.215 for predicting respiratory failure) 6 h before occurrence. This study suggests that AI consisting of simple vital signs and a brief interview could predict a cardiac arrest or acute respiratory failure 6 h earlier. View Full-Text
Keywords: deep learning; cardiac arrest; respiratory failure; intensive care unit; machine learning; artificial intelligence deep learning; cardiac arrest; respiratory failure; intensive care unit; machine learning; artificial intelligence
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Kim, J.; Chae, M.; Chang, H.-J.; Kim, Y.-A.; Park, E. Predicting Cardiac Arrest and Respiratory Failure Using Feasible Artificial Intelligence with Simple Trajectories of Patient Data. J. Clin. Med. 2019, 8, 1336.

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