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

Research Methodology - AI (46) Innovative Methods in Public Health and Surveillance (399) Machine Learning (2212) Diabetes (284) Diabetes Health Services and System Innovations (148) Diabetes Surveillance and Epidemiology (41) Diabetes-specific EHR Improvements (35) Tools, Programs and Algorithms (405) Clinical Informatics (1421)

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 (6) Tweetations (2) Metrics

Journals

  1. Husain G, Nasef D, Jose R, Mayer J, Bekbolatova M, Devine T, Toma M. SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models. Algorithms 2025;18(1):37 View
  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 2026;29(1):159 View
  4. Tan J, Quan L, Tan H, Goh S, Thumboo J, Au M, Bee Y, Tan J. Ensemble Machine Learning Models for Predicting Patients With High Usage: Model Validation and Economic Impact Analysis. JMIR Medical Informatics 2026;14:e77202 View
  5. Prashanthan A, Prashanthan J. Optimized Explainable Predictive Models for Risk-Based Prioritization in Type 2 Diabetes Prevention among Women with Prior Gestational Diabetes. Journal of Future Artificial Intelligence and Technologies 2026;2(4):734 View

Conference Proceedings

  1. Buizon H, Suba O, Simangan R, Magboo M, Magboo V. 2024 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT). Enhanced Pesticide Exposure Diagnosis: A Machine Learning Approach View