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

Machine Learning (1370) Clinical Information and Decision Making (1314) Decision Support for Health Professionals (1127) Artificial Intelligence in Diabetes Care and Prevention (21)

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 (9) 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