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

Formative Evaluation of Digital Health Interventions (2959) Clinical Informatics (1207) Clinical Information and Decision Making (1747) Machine Learning (1856)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41725, first published .
Machine Learning and Causal Approaches to Predict Readmissions and Its Economic Consequences Among Canadian Patients With Heart Disease: Retrospective Study

Machine Learning and Causal Approaches to Predict Readmissions and Its Economic Consequences Among Canadian Patients With Heart Disease: Retrospective Study

Machine Learning and Causal Approaches to Predict Readmissions and Its Economic Consequences Among Canadian Patients With Heart Disease: Retrospective Study

Authors of this article:

Ethan Rajkumar1 Author Orcid Image ;   Kevin Nguyen2 Author Orcid Image ;   Sandra Radic2 Author Orcid Image ;   Jubelle Paa2 Author Orcid Image ;   Qiyang Geng3 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Maisonnave M, Rajabi E, Taghavi M, VanBerkel P. Explainable machine learning to identify risk factors for unplanned hospital readmissions in Nova Scotian hospitals. Computers in Biology and Medicine 2025;190:110024 View
  2. Amiri-Ara M, Gheydani A, Yaghoubi M. Predicting Hospital Readmission Rates Using Data Mining Techniques. Hospital Topics 2025:1 View