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)

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...

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Bibliographic Details
Main Author: Tarik Elhadd (5480393) (author)
Other Authors: 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)
Published: 2020
Subjects:
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
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