Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality-Reference-Cited by-同舟云学术

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

Published:2020-11-17 Issue:1 Volume:8 Page:106-115
ISSN:2055-5822
Container-title:ESC Heart Failure
language:en
Short-container-title:ESC Heart Failure

Author:

Shin Sheojung¹^ORCID,Austin Peter C.¹^ORCID,Ross Heather J.¹^ORCID,Abdel‐Qadir Husam¹^ORCID,Freitas Cassandra¹,Tomlinson George¹,Chicco Davide¹^ORCID,Mahendiran Meera¹,Lawler Patrick R.¹,Billia Filio¹,Gramolini Anthony¹^ORCID,Epelman Slava¹^ORCID,Wang Bo¹,Lee Douglas S.¹^ORCID

Affiliation:

1. University of Toronto, ICES Rm G‐106, 2075 Bayview Ave. Toronto ON M4G2E1 Canada

Funder

University of Toronto

Heart and Stroke Foundation of Canada

Canadian Institutes of Health Research

Ontario Ministry of Health and Long-Term Care

Publisher

Wiley

Subject

Cardiology and Cardiovascular Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ehf2.13073

Reference49 articles.

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2. Heart Failure-Related Hospitalization in the U.S., 1979 to 2004

3. Epidemiology and aetiology of heart failure

4. Machine learning in cardiovascular medicine: are we there yet?

5. Predictive modeling of hospital readmission rates using electronic medical record‐wide machine learning: a case‐study using Mount Sinai Heart Failure Cohort;Shameer K;Pac Symp Biocomput,2017

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