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

Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (87) Machine Learning (2076) Viewpoints and Perspectives (619) Knowledge Translation and Implementation Science (513) Artificial Intelligence (2129) Digital Health, Telehealth and e-Innovation in Clinical Settings (556)

Published on in Vol 16 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50201, first published .
Applying Machine Learning Techniques to Implementation Science

Applying Machine Learning Techniques to Implementation Science

Applying Machine Learning Techniques to Implementation Science

Authors of this article:

Nathalie Huguet1, 2 Author Orcid Image ;   Jinying Chen3, 4, 5 Author Orcid Image ;   Ravi B Parikh6 Author Orcid Image ;   Miguel Marino1, 2 Author Orcid Image ;   Susan A Flocke1, 2 Author Orcid Image ;   Sonja Likumahuwa-Ackman1, 2 Author Orcid Image ;   Justin Bekelman6, 7 Author Orcid Image ;   Jennifer E DeVoe1, 2 Author Orcid Image
Article Authors Cited by (3) Tweetations (3) Metrics

Journals

  1. Matlin S, Claron I, Merone J, Netto G, Takian A, Zaman M, Saso L. Artificial Intelligence in migrant health: a critical perspective on opportunities and risks. The Lancet Regional Health - Europe 2025;57:101421 View
  2. Liu J, Ssewamala F, An R, Ji M. Use of Automated Machine Learning to Detect Undiagnosed Diabetes in US Adults: Development and Validation Study. JMIR AI 2025;4:e68260 View

Conference Proceedings

  1. Bari S, Shailee S, Joyanti F, Islam M. 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN). Dengue Outbreak Prediction in Bangladesh Based on Meteorological Factors View