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

Natural Language Processing (1320) Clinical Informatics (2365) Clinical Information and Decision Making (3850) New Methods (1846) Cost-Effectiveness and Economics (515) Decision Support for Health Professionals (2311) Health Services Research (685) Secondary Use of Clinical Data for Research and Surveillance (649) Machine Learning (3303) Theme Issue 2021: Health Natural Language Processing and Applications (26)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31442, first published .
Doctor in white coat holding stethoscope in clinic

Assessing the Value of Unsupervised Clustering in Predicting Persistent High Health Care Utilizers: Retrospective Analysis of Insurance Claims Data

Assessing the Value of Unsupervised Clustering in Predicting Persistent High Health Care Utilizers: Retrospective Analysis of Insurance Claims Data

Authors of this article:

Raghav Ramachandran1 Author Orcid Image ;   Michael J McShea1 Author Orcid Image ;   Stephanie N Howson1 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 (5) Tweetations (2) Metrics

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  1. Cowley H, Robinette M, Matelsky J, Xenes D, Kashyap A, Ibrahim N, Robinson M, Zeger S, Garibaldi B, Gray-Roncal W. Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients. Scientific Reports 2023;13(1) View
  2. Howson S, McShea M, Ramachandran R, Burkom H, Chang H, Weiner J, Kharrazi H. Improving the Prediction of Persistent High Health Care Utilizers: Retrospective Analysis Using Ensemble Methodology. JMIR Medical Informatics 2022;10(3):e33212 View
  3. Wang H, Wei T. Integrating machine learning into life cycle assessment: Review and future outlook. PLOS Climate 2025;4(10):e0000732 View
  4. Munishi C, Ruhago G, Mori A, Amos J, Haji O, Kagaigai A, Kengia J, Martin O. Prediction of insurance membership retention rates using machine learning: a case study of Tanzania’s improved community health insurance fund (iCHF). Discover Health Systems 2026;5(1) View
  5. 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