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

Formative Evaluation of Digital Health Interventions (2853) Machine Learning (1764) Methods and Feasibility Studies (398) Diabetes Self-Management (540) Glucose Tracking and Self-Monitoring of Blood Glucose (184)

Published on in Vol 6, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33329, first published .
Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study

Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study

Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study

Authors of this article:

Sowmya Kamath1 Author Orcid Image ;   Karthik Kappaganthu1 Author Orcid Image ;   Stefanie Painter1 Author Orcid Image ;   Anmol Madan1 Author Orcid Image
Article Authors Cited by (5) Tweetations (3) Metrics

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  1. Nyberg O, Klami A. Quantifying uncertainty of uplift: Trees and T-learners. Neurocomputing 2024;590:127741 View
  2. Barbaric A, Christofferson K, Benseler S, Lalloo C, Mariakakis A, Pham Q, Swart J, Yeung R, Cafazzo J. Health recommender systems to facilitate collaborative decision-making in chronic disease management: A scoping review. DIGITAL HEALTH 2025;11 View
  3. Li C, Petruchik O, Grishanina E, Kovalchuk S. Multi-agent norm perception and induction in distributed healthcare. Journal of Biomedical Informatics 2025;166:104835 View

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