Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes

Purpose: The objective of this study was to evaluate the efficacy of machine learning algorithms in predicting risks of complications and poor glycemic control in nonadherent type 2 diabetes (T2D). Materials and Methods: This study was a real-world study of the complications and blood glucose progno...

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Detalles Bibliográficos
Autores principales: Fan, Yuting, Long, Enwu, Cai, Lulu, Cao, Qiyuan, Wu, Xingwei, Tong, Rongsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Pharmacology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8258097/
https://www.ncbi.nlm.nih.gov/pubmed/34239440
http://dx.doi.org/10.3389/fphar.2021.665951

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8258097/
https://www.ncbi.nlm.nih.gov/pubmed/34239440
http://dx.doi.org/10.3389/fphar.2021.665951

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