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

Non-randomized Protocols and Methods (ehealth) (687) Diabetes (267) Health Equity (10) Machine Learning (2134)

Published on in Vol 14 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59308, first published .
Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study

Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study

Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study

Authors of this article:

Niteesh K Choudhry1 Author Orcid Image ;   Shweta Priyadarshini2 Author Orcid Image ;   Jaganath Swamy2 Author Orcid Image ;   Mridul Mehta2 Author Orcid Image
Article Authors Cited by (5) Tweetations Metrics

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  2. Arslan S. Artificial intelligence in food safety and nutrition practices: opportunities and risks. Academia Nutrition and Dietetics 2025;2(3) View
  3. Shephali S. The Role of Artificial Intelligence in Predicting Consumer Preferences and Personalizing Food Products: An Empirical Study in Bihar. Exploresearch 2025;02(03):84 View
  4. Marchianti A, Nandakumar A, Putri E, Rachmawati D, Sugiyanta S, Hairrudin H, Sakinah E, Firdaus J, Muniroh M, Nugraha E. Women’s Occupational Tobacco Dust Exposure in Indonesia (T-CHARM): Protocol for a Prospective Cohort Study. JMIR Research Protocols 2025;14:e84231 View
  5. Waly Y, Hussain A, Al-Majmuei A, Alatoom M, Alaraibi A, Alaysereen A, Deen G. Evolution of Biosensors and Current State-of-the-Art Applications in Diabetes Control. Biosensors 2026;16(1):39 View