Data-Driven Blood Glucose Pattern Classification and Anomalies Detection


Posted Jul 26 , 2019 01:50 AM

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes.
Woldaregay AZ, et al. J Med Internet Res. 2019;21:e11030

Institute Summary (excerpted from the abstract)

Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading because of either a precisely known reason (normal cause variation) or an unknown reason (special cause variation) to the patient. Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular. However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification and detection in people with diabetes. The purpose of this review is to identify, assess, and analyze the state-of-the-art machine-learning strategies and their hybrid systems focusing on BG anomaly classification and detection including glycemic variability (GV), hyperglycemia, and hypoglycemia in type 1 diabetes within the context of personalized decision support systems and BG alarm events applications, which are important constituents for optimal diabetes self-management. The authors conducted a literature search between September 1 and October 1, 2017, and October 15 and November 5, 2018, through various Web-based databases. Peer-reviewed journals and articles were considered. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming. The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed.

Why is this important?

While this article is quite technical in its discussion of approaches to the use of blood glucose and other data to describe or predict various aspects of diabetes control, it does demonstrate that there are numerous approaches that have been shown to work, and that the field of machine learning and its application to diabetes data is rapidly growing. Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual’s glycemic variability using different approaches. As these approaches become more mainstream, the hope is that individual patients can use their own data more effectively to improve their outcomes.

Concluding Thought:
"AI is a tool. The choice about how it gets deployed is ours.” - Oren Etzioni

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