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

Decision Support for Health Professionals (1657) Ontologies, Classifications, and Coding (350) Diabetes (287) Diabetes Prevention (131) Artificial Intelligence in Diabetes Care and Prevention (65) Generative Language Models Including ChatGPT (838) AI Language Models in Health Care (264)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/87374, first published .
Modeling Diabetes Risk and Progression With Public Health Data: Ontology-Guided, Simulation-Capable Digital Twin Study

Modeling Diabetes Risk and Progression With Public Health Data: Ontology-Guided, Simulation-Capable Digital Twin Study

Modeling Diabetes Risk and Progression With Public Health Data: Ontology-Guided, Simulation-Capable Digital Twin Study

Authors of this article:

Qingrui Li1 Author Orcid Image ;   Kapileshwor Ray Amat1 Author Orcid Image ;   Eric L Johnson2 Author Orcid Image ;   Juan Li1 Author Orcid Image
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Qingrui Li   1 ;   Kapileshwor Ray Amat   1 ;   Eric L Johnson   2 ;   Juan Li   1

1 Department of Computer Science, North Dakota State University, Fargo, ND, United States

2 Department of Family and Community Medicine, School of Medicine & Health Sciences, University of North Dakota, Grand Forks, ND, United States

Corresponding Author:

  • Juan Li
  • Department of Computer Science
  • North Dakota State University
  • PO Box 6050, NDSU Dept #2455
  • Fargo, ND 58108
  • United States
  • Phone: 1 7012319662
  • Email: j.li@ndsu.edu