AI and Machine Learning in Diabetes Management: Opportunity, Status, and Challenges

Marwa Qaraqe*, Madhav Erraguntla, Darpit Dave

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Citations (Scopus)

Abstract

Diabetes is a costly and burdensome metabolic disorder that occurs due to the elevated blood glucose levels. Poorly managed diabetes can lead to serious and life-threatening health complications. A person’s glycated hemoglobin (HbA1C or A1C) measures the average blood glucose for the past 2–3 months by measuring how much glucose is bound to the hemoglobin cells in the blood. The HbA1C is used both to diagnose diabetes and assess the effectiveness of a person’s management plan. Developing a model that can accurately predict a person’s future HbA1C 2–3 months in advance holds immense potential for preventative and tailored medical care. With the new era of artificial intelligence (AI) it becomes increasing evident that some of unanswered health issues can be unlocked by leveraging on advanced AI and machine learning algorithms. In addition, sudden plummeted or elevated blood glucose levels also pose serious and life-threating consequences to diabetic people. The development of a detection and prediction model capable of detecting or predicting instances of hyperglycemia or hypoglycemia using new CGM technology is critical. This chapter discusses the consequences of poorly managed diabetes and how a more personalized treatment plan for diabetes may lie in the detection of hyper/hypoglycemic events and the prediction of a person’s HbA1C using their current blood glucose values.

Original languageEnglish
Title of host publicationLecture Notes in Bioengineering
PublisherSpringer Science and Business Media Deutschland GmbH
Pages129-141
Number of pages13
DOIs
Publication statusPublished - 2021

Publication series

NameLecture Notes in Bioengineering
ISSN (Print)2195-271X
ISSN (Electronic)2195-2728

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