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

Machine Learning (1952) Artificial Intelligence (1983) Adoption and Change Management of eHealth Systems (862) Clinical Information and Decision Making (1802)

Published on in Vol 10, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37578, first published .
Predicting Readmission Charges Billed by Hospitals: Machine Learning Approach

Predicting Readmission Charges Billed by Hospitals: Machine Learning Approach

Predicting Readmission Charges Billed by Hospitals: Machine Learning Approach

Authors of this article:

Deepika Gopukumar1 Author Orcid Image ;   Abhijeet Ghoshal2 Author Orcid Image ;   Huimin Zhao3 Author Orcid Image
Article Authors Cited by (10) Tweetations (2) Metrics

Journals

  1. Ru B, Tan X, Liu Y, Kannapur K, Ramanan D, Kessler G, Lautsch D, Fonarow G. 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. JMIR Formative Research 2023;7:e41775 View
  2. Lashen H, St John T, Almallah Y, Sasidhar M, Shamout F. Machine Learning Models Versus the National Early Warning Score System for Predicting Deterioration: Retrospective Cohort Study in the United Arab Emirates. JMIR AI 2023;2:e45257 View
  3. Wang S, Barrette L, Ng J, Sangal N, Cannady S, Brody R, Bur A, Brant J. Predicting reoperation and readmission for head and neck free flap patients using machine learning. Head & Neck 2024;46(8):1999 View
  4. Aggarwal S, Singh R, Rathore A, Kapoor K, Patel M. A Novel Data-driven Machine Learning Techniques to Predict Compressive Strength of Fly Ash and Recycled Coarse Aggregates Based Self-Compacting Concrete. Materials Today Communications 2024:109294 View
  5. Fan S, Abulizi A, You Y, Huang C, Yimit Y, Li Q, Zou X, Nijiati M. Predicting hospitalization costs for pulmonary tuberculosis patients based on machine learning. BMC Infectious Diseases 2024;24(1) View
  6. Long H, Chen Y, Feng J, Chen J, Zhang X, Han W, Kang M, Du X, WANG L. Annual global dengue dynamics are related to multi-source factors revealed by a machine learning prediction analysis. PLOS Neglected Tropical Diseases 2025;19(6):e0013232 View
  7. Li X, Tia M, Bos J, Schuiling-Veninga C, Hak E, Mubarik S. LASSO logistic regression and cluster analysis in predicting adherence and drug patterns among new users of monotherapy for antihypertensive drugs. Journal of Hypertension 2025;43(9):1519 View
  8. Shouri S, De la Sen M, Gordji M. Designing a Smart Health Insurance Pricing System: Integrating XGBoost and Repeated Nash Equilibrium in a Sustainable, Data-Driven Framework. Information 2025;16(9):733 View
  9. Kim J, Jeong Y, Kim S, Jun S, Park J, Hoe H, Song J. A Novel Approach Using Serious Game Data to Predict the WISC-V Processing Speed Index in Children with Attention Deficit/Hyperactivity Disorder: A Machine Learning Study (Preprint). JMIR Serious Games 2025 View

Books/Policy Documents

  1. Ragunathan B, Bachmann N, Dornberger R, Hanne T. Proceedings of International Conference on Computational Intelligence. View