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

Machine Learning (3350) Cost-Effectiveness and Economics (520) Decision Support for Health Professionals (2357) Electronic Health Records (1799)

Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33212, first published .
Two female doctors in white coats reviewing patient data on a tablet.

Improving the Prediction of Persistent High Health Care Utilizers: Retrospective Analysis Using Ensemble Methodology

Improving the Prediction of Persistent High Health Care Utilizers: Retrospective Analysis Using Ensemble Methodology

Authors of this article:

Stephanie N Howson1 Author Orcid Image ;   Michael J McShea1 Author Orcid Image ;   Raghav Ramachandran1 Author Orcid Image ;   Howard S Burkom1 Author Orcid Image ;   Hsien-Yen Chang2 Author Orcid Image ;   Jonathan P Weiner2 Author Orcid Image ;   Hadi Kharrazi2 Author Orcid Image
Article Authors Cited by (8) Tweetations (2) Metrics

Journals

  1. Ma Y, Tu X, Luo X, Hu L, Wang C. Machine-learning-based cost prediction models for inpatients with mental disorders in China. BMC Psychiatry 2025;25(1) View
  2. Zhang P, Zhu B, Chen X, Wang L. Predicting high-need high-cost pediatric hospitalized patients in China based on machine learning methods. Scientific Reports 2025;15(1) View
  3. Sanford Kobayashi E, Tobin L, Arenchild M, Benson W, Coufal N, Juarez E, Kingsmore S, Knight J, Lenberg J, Schwarz A, Vargas-Shiraishi O, Wigby K, Bainbridge M. Long-term follow-up of children who received rapid genomic sequencing. Genetics in Medicine 2025;27(6):101403 View
  4. Koh J, Tan Y, Oh H, Poon B. Factors associated with persistent high healthcare service utilisers in Singapore: A population health analysis. Annals of the Academy of Medicine, Singapore 2025;54(8):476 View
  5. Jelveh Z, Tentner A, Beberman X, Pollack H. Predicting behavioral health-involved police encounters: A machine learning approach. Journal of Quantitative Criminology 2026 View
  6. Nkosi T, Molefe L, Dlamini S, Mokoena A, Ndlovu K. Explainable Risk Stratification Model for Prioritizing Case Management Referrals Using Prior Utilization, Chronic Disease Burden, Social Needs Documentation, Missed Appointments, and Care Gap Indicators. Journal of Health Informatics and Digital Systems 2025;5(1) View
  7. Kowalska A, Nowak P, Zielinski T, Mazur K. Multimodal Machine Learning Model for Predicting High-Cost Hospital Episodes Using Pharmacy Utilization, Procedure Sequences, Length-of-Stay Trends, Intensive Care Transfers, and Administrative Claims Data. Journal of Health Informatics and Digital Systems 2024;4(2) View

Conference Proceedings

  1. Nayak S. 2025 18th International Conference on Development in eSystem Engineering (DeSE). Hospital Billing Cost Prediction for High-Cost, High-Need Patients Using Regression Analysis and Neural Networks with Explainable AI View