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).

Journal: Diabetes research and clinical practice
PMID: 32858096

Abstract

OBJECTIVE: 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 who fast during Ramadan.

Authors

  • Tarik Elhadd
    Qatar Metabolic Institute, Qatar. Electronic address: tarikelhadd58@gmail.com.
  • Raghvendra Mall
    Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
  • Mohammed Bashir
    Qatar Metabolic Institute, Qatar.
  • Joao Palotti
    Qatar Computer Research Institute (QCRI), Doha, Qatar; Hamad Medical Corporation, Doha, Qatar; CSAIL, Massachusetts Institute of Technology, USA.
  • Luis Fernandez-Luque
    Working Groups and SIGs, International Medical Informatics Association; Graduate Institute of Biomedical Informatics, College of Medical Science & Technology, Taipei Medical University, Taipei, Taiwan; Salumedia Labs, Sevilla, Spain.
  • Faisal Farooq
    Qatar Computer Research Institute (QCRI), Doha, Qatar.
  • Dabia Al Mohanadi
    Qatar Metabolic Institute, Qatar.
  • Zainab Dabbous
    Qatar Metabolic Institute, Qatar.
  • Rayaz A Malik
    Weill Cornell Medicine-Qatar, Research Division, Doha, Qatar.
  • Abdul Badi Abou-Samra
    Qatar Metabolic Institute, Qatar.

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