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

Machine Learning (1518) Decision Support for Health Professionals (1222) Electronic Health Records (1074) Cost-Effectiveness and Economics (261)

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 Tweetations (2) Metrics

Stephanie N Howson   1 , MSc ;   Michael J McShea   1 , MSc ;   Raghav Ramachandran   1 , PhD ;   Howard S Burkom   1 , PhD ;   Hsien-Yen Chang   2 , PhD ;   Jonathan P Weiner   2 , DrPH ;   Hadi Kharrazi   2 , MD, PhD

1 Applied Physics Laboratory, Johns Hopkins University, Baltimore, MD, United States

2 Center for Population Health Information Technology, Johns Hopkins School of Public Health, Baltimore, MD, United States

Corresponding Author:

  • Hadi Kharrazi, MD, PhD
  • Center for Population Health Information Technology
  • Johns Hopkins School of Public Health
  • 624 N Broadway
  • Office 606
  • Baltimore, MD, 21205-1900
  • United States
  • Phone: 1 443 287 8264
  • Email: kharrazi@jhu.edu