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

Bioinformatics, genomics, tools and databases (23) Machine Learning (1704) Artificial Intelligence (1689) Decision Support for Health Professionals (1327) Clinical Information and Decision Making (1622) Big Data (278) Electronic Health Records (1166)

Published on in Vol 3, No 1 (2022): Jan-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37951, first published .
Treatment Discontinuation Prediction in Patients With Diabetes Using a Ranking Model: Machine Learning Model Development

Treatment Discontinuation Prediction in Patients With Diabetes Using a Ranking Model: Machine Learning Model Development

Treatment Discontinuation Prediction in Patients With Diabetes Using a Ranking Model: Machine Learning Model Development

Authors of this article:

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

Journals

  1. Kurasawa H, Waki K, Seki T, Chiba A, Fujino A, Hayashi K, Nakahara E, Haga T, Noguchi T, Ohe K. Enhancing Type 2 Diabetes Treatment Decisions With Interpretable Machine Learning Models for Predicting Hemoglobin A1c Changes: Machine Learning Model Development. JMIR AI 2024;3:e56700 View
  2. Kilroy D, Healy G, Caton S, Rana T. Prediction of future customer needs using machine learning across multiple product categories. PLOS ONE 2024;19(8):e0307180 View
  3. Kanyongo W, Ezugwu A, Moyo T, Fonou Dombeu J. Machine learning-based classification of medication adherence among patients with noncommunicable diseases. Informatics in Medicine Unlocked 2025;52:101611 View
  4. Yismaw M, Tafere C, Tefera B, Demsie D, Feyisa K, Addisu Z, Zeleke T, Siraj E, Worku M, Berihun F. Artificial intelligence based predictive tools for identifying type 2 diabetes patients at high risk of treatment Non-adherence: A systematic review. International Journal of Medical Informatics 2025;198:105858 View