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Published on in Vol 11 (2025)

This is a member publication of University of Toronto

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68952, first published .
Machine Learning Applications in Population and Public Health: Guidelines for Development, Testing, and Implementation

Machine Learning Applications in Population and Public Health: Guidelines for Development, Testing, and Implementation

Machine Learning Applications in Population and Public Health: Guidelines for Development, Testing, and Implementation

Journals

  1. Nwokedi V, Ezeamii P, Olowookere A, Omolabake O. Integrating Real-Time Genomic Surveillance (Next-Generation Sequencing) with Epidemiological Models for Infectious Disease Intervention Planning. Epidemiology and Health Data Insights 2026;2(2):ehdi030 View
  2. Abdalla S, Galea S. Embracing complexity and innovation to tackle the social determinants of health. BMJ Global Health 2024;9(Suppl 1):e020610 View
  3. Wu E, Balise R, Katz B, Harris D, Bullard M, Fareed N, Larochelle M, Villani J. Building Public Health Data Dashboards: Tutorial Playbook. JMIR Public Health and Surveillance 2026;12:e83157 View
  4. de Souza-Lima J, Yáñez-Sepúlveda R, Giakoni-Ramírez F, Muñoz-Strale C, Alarcon-Aguilar J, Parra-Saldias M, Duclos-Bastias D, Godoy-Cumillaf A, Merellano-Navarro E, Bruneau-Chávez J, Farias-Valenzuela C. Predicting Physical Inactivity in Chilean Adults: A Comparison of Survey-Weighted Logistic Regression and Explainable Machine Learning Models. Data 2026;11(4):73 View