Value of laboratory results in addition to vital signs in a machine learning algorithm to predict in-hospital cardiac arrest: A single-center retrospective cohort study

Although machine learning-based prediction models for in-hospital cardiac arrest (IHCA) have been widely investigated, it is unknown whether a model based on vital signs alone (Vitals-Only model) can perform similarly to a model that considers both vital signs and laboratory results (Vitals+Labs mod...

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Bibliographic Details
Published in:	PLOS ONE Vol. 15; no. 7; p. e0235835
Main Authors:	Ueno, Ryo, Xu, Liyuan, Uegami, Wataru, Matsui, Hiroki, Okui, Jun, Hayashi, Hiroshi, Miyajima, Toru, Hayashi, Yoshiro, Pilcher, David, Jones, Daryl
Format:	Journal Article
Language:	English
Published:	San Francisco Public Library of Science (PLoS) 13.07.2020 Public Library of Science
Subjects:	Age Aged Algorithms Blood pressure Cardiac arrest Care and treatment Cohort analysis Computer and Information Sciences Confidence intervals Demography Diagnosis Education Female Graduate studies Health care Health risk assessment Heart Heart Arrest Hemoglobin Hospitalization Hospitals Humans Intensive care Laboratories Learning algorithms Machine Learning Male Medicine Medicine and Health Sciences Middle Aged Observations Patients Performance prediction Physical Sciences Prediction models Preventive medicine Prognosis Public health Q R Research and Analysis Methods Research Article Retrospective Studies Science Sensitivity analysis Statistical analysis Technology application Training Variables Vital Signs Japan New Zealand Melbourne Victoria Australia Australia
ISSN:	1932-6203, 1932-6203
Online Access:	Get full text
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