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

Research Methodology - AI (27) Innovative Methods in Public Health and Surveillance (370) Machine Learning (1971) Diabetes (245) Diabetes Health Services and System Innovations (137) Diabetes Surveillance and Epidemiology (36) Diabetes-specific EHR Improvements (34) Tools, Programs and Algorithms (358) Clinical Informatics (1257)

Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58463, first published .
Machine Learning–Based Prediction for High Health Care Utilizers by Using a Multi-Institutional Diabetes Registry: Model Training and Evaluation

Machine Learning–Based Prediction for High Health Care Utilizers by Using a Multi-Institutional Diabetes Registry: Model Training and Evaluation

Machine Learning–Based Prediction for High Health Care Utilizers by Using a Multi-Institutional Diabetes Registry: Model Training and Evaluation

Authors of this article:

Joshua Kuan Tan1 Author Orcid Image ;   Le Quan2 Author Orcid Image ;   Nur Nasyitah Mohamed Salim1 Author Orcid Image ;   Jen Hong Tan2 Author Orcid Image ;   Su-Yen Goh3 Author Orcid Image ;   Julian Thumboo1 Author Orcid Image ;   Yong Mong Bee3 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

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  2. 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
  3. Gallego-Moll C, Carrasco-Ribelles L, Casajuana M, Maynou L, Arocena P, Violán C, Zabaleta-del-Olmo E. Predicting Healthcare Utilization Outcomes With Artificial Intelligence: A Large Scoping Review. Value in Health 2025 View

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