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

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

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...

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Bibliographic Details
Main Authors: Jeongmin Kim, Myunghun Chae, Hyuk-Jae Chang, Young-Ah Kim, Eunjeong Park
Format: Article
Language:English
Published: MDPI AG 2019-08-01
Series:Journal of Clinical Medicine
Subjects:
deep learning
cardiac arrest
respiratory failure
intensive care unit
machine learning
artificial intelligence
Online Access:https://www.mdpi.com/2077-0383/8/9/1336

Internet

https://www.mdpi.com/2077-0383/8/9/1336

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