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

Foundations of AI (57) Artificial Intelligence (2064) Applications of AI (272)

Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56700, first published .
Enhancing Type 2 Diabetes Treatment Decisions With Interpretable Machine Learning Models for Predicting Hemoglobin A1c Changes: Machine Learning Model Development

Enhancing Type 2 Diabetes Treatment Decisions With Interpretable Machine Learning Models for Predicting Hemoglobin A1c Changes: Machine Learning Model Development

Enhancing Type 2 Diabetes Treatment Decisions With Interpretable Machine Learning Models for Predicting Hemoglobin A1c Changes: Machine Learning Model Development

Authors of this article:

Hisashi Kurasawa1, 2 Author Orcid Image ;   Kayo Waki2 Author Orcid Image ;   Tomohisa Seki2 Author Orcid Image ;   Akihiro Chiba1, 3 Author Orcid Image ;   Akinori Fujino1 Author Orcid Image ;   Katsuyoshi Hayashi1 Author Orcid Image ;   Eri Nakahara1, 2 Author Orcid Image ;   Tsuneyuki Haga1, 4 Author Orcid Image ;   Takashi Noguchi5 Author Orcid Image ;   Kazuhiko Ohe2 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. Sim H, Ha H, Kim H. Integrated biomarker analysis and next-generation AI for precision diabetes prediction. Toxicological Research 2025 View
  2. Ge L, Zhang Y, Peng G, Long M, Jin T, Lu B, Shao J, Li X. Serum alpha-1-microglobulin as a predictor of multiple complications in type 2 diabetes mellitus patients. World Journal of Diabetes 2025;16(10) View