Predicting short‐ and long‐term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine‐learning algorithms

AIM: To assess the potential of supervised machine‐learning techniques to identify clinical variables for predicting short‐term and long‐term glycated haemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS: We included...

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Detalles Bibliográficos
Autores principales: Nagaraj, Sunil B., Sidorenkov, Grigory, van Boven, Job F. M., Denig, Petra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Blackwell Publishing Ltd 2019
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6899933/
https://www.ncbi.nlm.nih.gov/pubmed/31453664
http://dx.doi.org/10.1111/dom.13860

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6899933/
https://www.ncbi.nlm.nih.gov/pubmed/31453664
http://dx.doi.org/10.1111/dom.13860

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