This paper is in the following e-collection/theme issue:

Formative Studies and eHealth/mHealth Development (515) Clinical Informatics (912) Clinical Information and Decision Making (1328) Decision Support for Health Professionals (1137) Machine Learning (1385) Cardiology (130) Cardiovascular Disease Prevention (114)

Published on in Vol 13 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52744, first published .
Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records: Protocol for a Quasi-Experimental Study for Impact Assessment

Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records: Protocol for a Quasi-Experimental Study for Impact Assessment

Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records: Protocol for a Quasi-Experimental Study for Impact Assessment

Authors of this article:

Monika Nair1 Author Orcid Image ;   Lina E Lundgren2 Author Orcid Image ;   Amira Soliman3 Author Orcid Image ;   Petra Dryselius4 Author Orcid Image ;   Ebba Fogelberg4 Author Orcid Image ;   Marcus Petersson4 Author Orcid Image ;   Omar Hamed3 Author Orcid Image ;   Miltiadis Triantafyllou5 Author Orcid Image ;   Jens Nygren1 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. Huisman T, Huisman T. Artificial Intelligence in Newborn Medicine. Newborn 2024;3(2):96 View