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

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 algorith...

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Detalles Bibliográficos
Autores principales: Shin, Sheojung, Austin, Peter C., Ross, Heather J., Abdel‐Qadir, Husam, Freitas, Cassandra, Tomlinson, George, Chicco, Davide, Mahendiran, Meera, Lawler, Patrick R., Billia, Filio, Gramolini, Anthony, Epelman, Slava, Wang, Bo, Lee, Douglas S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Original Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7835549/
https://www.ncbi.nlm.nih.gov/pubmed/33205591
http://dx.doi.org/10.1002/ehf2.13073

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7835549/
https://www.ncbi.nlm.nih.gov/pubmed/33205591
http://dx.doi.org/10.1002/ehf2.13073

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