Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast during ramadan (The PROFAST – IT Ramadan study)
<h3>Objective</h3><p dir="ltr">To develop a machine-based algorithm from clinical and demographic data, physical activity and glucose variability to predict hyperglycaemic and hypoglycaemic excursions in patients with type 2 diabetes on multiple glucose lowering therapies...
محفوظ في:
| المؤلف الرئيسي: | Tarik Elhadd (5480393) (author) |
|---|---|
| مؤلفون آخرون: | Raghvendra Mall (581171) (author), Mohammed Bashir (5593550) (author), Joao Palotti (8479842) (author), Luis Fernandez-Luque (3572423) (author), Faisal Farooq (13134579) (author), Dabia Al Mohanadi (17100238) (author), Zainab Dabbous (17100241) (author), Rayaz A. Malik (7372649) (author), Abdul Badi Abou-Samra (9417977) (author) |
| منشور في: |
2020
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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مواد مشابهة
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