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

Artificial Intelligence (1371) Machine Learning (1465) Clinical Information and Decision Making (1407) Cardiology (159)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46934, first published .
The Price of Explainability in Machine Learning Models for 100-Day Readmission Prediction in Heart Failure: Retrospective, Comparative, Machine Learning Study

The Price of Explainability in Machine Learning Models for 100-Day Readmission Prediction in Heart Failure: Retrospective, Comparative, Machine Learning Study

The Price of Explainability in Machine Learning Models for 100-Day Readmission Prediction in Heart Failure: Retrospective, Comparative, Machine Learning Study

Authors of this article:

Amira Soliman1 Author Orcid Image ;   Björn Agvall2, 3 Author Orcid Image ;   Kobra Etminani1, 2 Author Orcid Image ;   Omar Hamed1 Author Orcid Image ;   Markus Lingman1, 2, 4, 5 Author Orcid Image
Article Authors Cited by (6) Tweetations (3) Metrics

Journals

  1. Terranova C, Cestonaro C, Fava L, Cinquetti A. AI and professional liability assessment in healthcare. A revolution in legal medicine?. Frontiers in Medicine 2024;10 View
  2. Nair M, Lundgren L, Soliman A, Dryselius P, Fogelberg E, Petersson M, Hamed O, Triantafyllou M, Nygren J. 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. JMIR Research Protocols 2024;13:e52744 View
  3. Gao Z, Liu X, Kang Y, Hu P, Zhang X, Yan W, Yan M, Yu P, Zhang Q, Xiao W, Zhang Z. Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model. Journal of Medical Internet Research 2024;26:e54363 View
  4. Chang T, Xia H, Mahajan S, Mahajan R, Maisog J, Vattikuti S, Chow C, Chang J, Chen R. Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to reduce preventable all-cause readmissions or death. PLOS ONE 2024;19(5):e0302871 View
  5. Budu E, Etminani K, Soliman A, Rögnvaldsson T. Evaluation of synthetic electronic health records: A systematic review and experimental assessment. Neurocomputing 2024;603:128253 View

Books/Policy Documents

  1. Sjöström J, Dryselius P, Nygren J, Nair M, Soliman A, Lundgren L. Design Science Research for a Resilient Future. View