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

Formative Evaluation of Digital Health Interventions (2491) Heart Failure Self-Management (155) Machine Learning (1501) Telehealth and Telemonitoring (1713) e-Cardiology and Telemonitoring in Cardiology (172) Decision Support for Health Professionals (1210) Clinical Informatics (1050)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41775, first published .
Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study

Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study

Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study

Authors of this article:

Boshu Ru1 Author Orcid Image ;   Xi Tan1 Author Orcid Image ;   Yu Liu1 Author Orcid Image ;   Kartik Kannapur2 Author Orcid Image ;   Dheepan Ramanan2 Author Orcid Image ;   Garin Kessler2, 3 Author Orcid Image ;   Dominik Lautsch1 Author Orcid Image ;   Gregg Fonarow4 Author Orcid Image
Article Authors Cited by (13) Tweetations (5) Metrics

Journals

  1. Han S, Sohn T, Ng B, Park C. Predicting unplanned readmission due to cardiovascular disease in hospitalized patients with cancer: a machine learning approach. Scientific Reports 2023;13(1) View
  2. Gargi J Trivedi , Rajesh Sanghvi . Novel Approach to Multi-Modal Image Fusion using Modified Convolutional Layers. Journal of Innovative Image Processing 2023;5(3):229 View
  3. Jahangiri S, Abdollahi M, Rashedi E, Azadeh-Fard N. A machine learning model to predict heart failure readmission: toward optimal feature set. Frontiers in Artificial Intelligence 2024;7 View
  4. Odrobina I. Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature Review. Diagnostics 2024;14(4):443 View
  5. Yu M, Son Y. Machine learning–based 30-day readmission prediction models for patients with heart failure: a systematic review. European Journal of Cardiovascular Nursing 2024;23(7):711 View
  6. Xu Z, Hu Y, Shao X, Shi T, Yang J, Wan Q, Liu Y. The Efficacy of Machine Learning Models for Predicting the Prognosis of Heart Failure: A Systematic Review and Meta-Analysis. Cardiology 2024:1 View
  7. Sirocchi C, Bogliolo A, Montagna S. Medical-informed machine learning: integrating prior knowledge into medical decision systems. BMC Medical Informatics and Decision Making 2024;24(S4) View
  8. Wu D, Shi Y, Wang C, Li C, Lu Y, Wang C, Zhu W, Sun T, Han J, Zheng Y, Zhang L. Investigating the impact of extreme weather events and related indicators on cardiometabolic multimorbidity. Archives of Public Health 2024;82(1) View
  9. Visco V, Robustelli A, Loria F, Rispoli A, Palmieri F, Bramanti A, Carrizzo A, Vecchione C, Palmieri F, Ciccarelli M, D’Angelo G. An explainable model for predicting Worsening Heart Failure based on genetic programming. Computers in Biology and Medicine 2024;182:109110 View
  10. Sun Z, Wang Z, Yun Z, Sun X, Lin J, Zhang X, Wang Q, Duan J, Huang L, Li L, Yao K. Machine learning‐based model for worsening heart failure risk in Chinese chronic heart failure patients. ESC Heart Failure 2024 View
  11. Hinrichs N, Meyer A, Koehler K, Kaas T, Hiddemann M, Spethmann S, Balzer F, Eickhoff C, Falk V, Hindricks G, Dagres N, Koehler F. Artificial intelligence based real-time prediction of imminent heart failure hospitalisation in patients undergoing non-invasive telemedicine. Frontiers in Cardiovascular Medicine 2024;11 View
  12. Kwak D, Liang Y, Shi X, Tan X. Comparing Machine Learning and Advanced Methods with Traditional Methods to Generate Weights in Inverse Probability of Treatment Weighting: The INFORM Study. Pragmatic and Observational Research 2024;Volume 15:173 View
  13. Hu Y, Ma F, Hu M, Shi B, Pan D, Ren J. Development and validation of a machine learning model to predict the risk of readmission within one year in HFpEF patients. International Journal of Medical Informatics 2025;194:105703 View