An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest.
Journal:
Journal of the American Heart Association
Published Date:
Jun 26, 2018
Abstract
BACKGROUND: In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-and-trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high false-alarm rates. We propose a deep learning-based early warning system that shows higher performance than the existing track-and-trigger systems.
Authors
Keywords
Adult
Aged
Decision Support Techniques
Deep Learning
Diagnosis, Computer-Assisted
Early Diagnosis
Female
Heart Arrest
Humans
Inpatients
Male
Middle Aged
Predictive Value of Tests
Prognosis
Reproducibility of Results
Resuscitation
Retrospective Studies
Risk Assessment
Risk Factors
Seoul
Time Factors
Vital Signs