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

Machine Learning (1971) Decision Support for Health Professionals (1468) Electronic Health Records (1271) Cost-Effectiveness and Economics (333)

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 .
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

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 (4) Tweetations (2) Metrics

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  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