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

Machine Learning (1501) Clinical Information and Decision Making (1434) Decision Support for Health Professionals (1210) Artificial Intelligence in Diabetes Care and Prevention (29)

Published on in Vol 9, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22148, first published .
Machine Learning Approach to Decision Making for Insulin Initiation in Japanese Patients With Type 2 Diabetes (JDDM 58): Model Development and Validation Study

Machine Learning Approach to Decision Making for Insulin Initiation in Japanese Patients With Type 2 Diabetes (JDDM 58): Model Development and Validation Study

Machine Learning Approach to Decision Making for Insulin Initiation in Japanese Patients With Type 2 Diabetes (JDDM 58): Model Development and Validation Study

Authors of this article:

Kazuya Fujihara1 Author Orcid Image ;   Yasuhiro Matsubayashi1 Author Orcid Image ;   Mayuko Harada Yamada1 Author Orcid Image ;   Masahiko Yamamoto1 Author Orcid Image ;   Toshihiro Iizuka2 Author Orcid Image ;   Kosuke Miyamura2 Author Orcid Image ;   Yoshinori Hasegawa2 Author Orcid Image ;   Hiroshi Maegawa3 Author Orcid Image ;   Satoru Kodama1 Author Orcid Image ;   Tatsuya Yamazaki4 Author Orcid Image ;   Hirohito Sone1 Author Orcid Image
Article Authors Cited by (10) Tweetations (3) Metrics

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  2. Nwanosike E, Conway B, Merchant H, Hasan S. Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. International Journal of Medical Informatics 2022;159:104679 View
  3. Morgan-Benita J, Galván-Tejada C, Cruz M, Galván-Tejada J, Gamboa-Rosales H, Arceo-Olague J, Luna-García H, Celaya-Padilla J. Hard Voting Ensemble Approach for the Detection of Type 2 Diabetes in Mexican Population with Non-Glucose Related Features. Healthcare 2022;10(8):1362 View
  4. Fujihara K, Sone H. Machine Learning Approach to Drug Treatment Strategy for Diabetes Care. Diabetes & Metabolism Journal 2023;47(3):325 View
  5. Zou X, Liu Y, Ji L. Review: Machine learning in precision pharmacotherapy of type 2 diabetes—A promising future or a glimpse of hope?. DIGITAL HEALTH 2023;9 View
  6. Khodve G, Banerjee S. Artificial Intelligence in Efficient Diabetes Care. Current Diabetes Reviews 2023;19(9) View
  7. Kumar A, Bawa S, Kumar N. iDP: ML-driven diabetes prediction framework using deep-ensemble modeling. Neural Computing and Applications 2024;36(5):2525 View
  8. Kim D, Choi H, Lee D, Kim M, Kim Y, Han S, Heo Y, Park J, Park J. A Deep Learning–Based Approach for Prediction of Vancomycin Treatment Monitoring: Retrospective Study Among Patients With Critical Illness. JMIR Formative Research 2024;8:e45202 View
  9. Eghbali-Zarch M, Masoud S. Application of machine learning in affordable and accessible insulin management for type 1 and 2 diabetes: A comprehensive review. Artificial Intelligence in Medicine 2024;151:102868 View
  10. Price J, Fujihara K, Kodama S, Yamazaki K, Maegawa H, Yamazaki T, Sone H. Machine learning algorithms mimicking specialists decision making on initial treatment for people with type 2 diabetes mellitus in Japan diabetes data management study (JDDM76). Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2024;18(11-12):103168 View