Comparison of machine learning methods with logistic regression analysis in creating predictive models for risk of critical in-hospital events in COVID-19 patients on hospital admission

Comparison of machine learning methods with logistic regression analysis in creating predictive models for risk of critical in-hospital events in COVID-19 patients on hospital admission

Abstract Background Machine learning (ML) algorithms have been trained to early predict critical in-hospital events from COVID-19 using patient data at admission, but little is known on how their performance compares with each other and/or with statistical logistic regression (LR). This prospective...

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Bibliographic Details
Main Authors: Aaron W. Sievering, Peter Wohlmuth, Nele Geßler, Melanie A. Gunawardene, Klaus Herrlinger, Berthold Bein, Dirk Arnold, Martin Bergmann, Lorenz Nowak, Christian Gloeckner, Ina Koch, Martin Bachmann, Christoph U. Herborn, Axel Stang
Format: Article
Language:English
Published: BMC 2022-11-01
Series:BMC Medical Informatics and Decision Making
Subjects:
COVID-19
Machine learning
Predictive models
Critical event prediction
Clinical decision-making
Online Access:https://doi.org/10.1186/s12911-022-02057-4

Internet

https://doi.org/10.1186/s12911-022-02057-4

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