An intelligent warning model for early prediction of cardiac arrest in sepsis patients.
Journal:
Computer methods and programs in biomedicine
PMID:
31416562
Abstract
BACKGROUND: Sepsis-associated cardiac arrest is a common issue with the low survival rate. Early prediction of cardiac arrest can provide the time required for intervening and preventing its onset in order to reduce mortality. Several studies have been conducted to predict cardiac arrest using machine learning. However, no previous research has used machine learning for predicting cardiac arrest in adult sepsis patients. Moreover, the potential of some techniques, including ensemble algorithms, has not yet been addressed in improving the prediction outcomes. It is required to find methods for generating high-performance predictions with sufficient time lapse before the arrest. In this regard, various variables and parameters should also been examined.
Authors
Keywords
Adolescent
Adult
Algorithms
APACHE
Case-Control Studies
Decision Trees
Electronic Health Records
Female
Heart Arrest
Humans
Incidence
Machine Learning
Male
Middle Aged
Monitoring, Ambulatory
Multivariate Analysis
Normal Distribution
Reproducibility of Results
Sensitivity and Specificity
Sepsis
Severity of Illness Index
Vital Signs
Young Adult