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...

وصف كامل

محفوظ في:

التفاصيل البيبلوغرافية
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
الموضوعات:	Biomedical and clinical sciences Clinical sciences Information and computing sciences Artificial intelligence Machine learning Diabetes mellitus Ramadan Type-2 diabetes Flash glucose monitoring system Hypoglycaemia Hyperglycaemia
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

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)

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