Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality

Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality

Abstract Aims This study aimed to review the performance of machine learning (ML) methods compared with conventional statistical models (CSMs) for predicting readmission and mortality in patients with heart failure (HF) and to present an approach to formally evaluate the quality of studies using ML...

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Bibliographic Details
Main Authors: Sheojung Shin, Peter C. Austin, Heather J. Ross, Husam Abdel‐Qadir, Cassandra Freitas, George Tomlinson, Davide Chicco, Meera Mahendiran, Patrick R. Lawler, Filio Billia, Anthony Gramolini, Slava Epelman, Bo Wang, Douglas S. Lee
Format: Article
Language:English
Published: Wiley 2021-02-01
Series:ESC Heart Failure
Subjects:
Machine learning
Heart failure
Readmission
Mortality
Hospitalization
Death
Online Access:https://doi.org/10.1002/ehf2.13073

Internet

https://doi.org/10.1002/ehf2.13073

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