Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes

PURPOSE: To gain an understanding of the heterogeneous group of type 2 diabetes (T2D) patients, we aimed to identify patients with the homogenous long-term HbA1c trajectories and to predict the trajectory membership for each patient using explainable machine learning methods and different clinical-,...

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Detalles Bibliográficos
Autores principales: Lavikainen, Piia, Chandra, Gunjan, Siirtola, Pekka, Tamminen, Satu, Ihalapathirana, Anusha T, Röning, Juha, Laatikainen, Tiina, Martikainen, Janne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9829833/
https://www.ncbi.nlm.nih.gov/pubmed/36636731
http://dx.doi.org/10.2147/CLEP.S380828

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9829833/
https://www.ncbi.nlm.nih.gov/pubmed/36636731
http://dx.doi.org/10.2147/CLEP.S380828

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