Abstract
Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.
Highlights
Type 1 diabetes (T1D) is a medical condition caused by deficient insulin production and results in dysregulation of blood glucose
An effective decision support systems (DSSs) is one which can increase the percent of time that the person with T1D spends in a target glucose range or reduce the percent of time spent in hypoglycemia
We have provided a comprehensive review of (1) DSS algorithms that provide insulin dosing recommendations to people using multiple daily injection (MDI) or continuous subcutaneous insulin infusion (CSII)
Summary
Type 1 diabetes (T1D) is a medical condition caused by deficient insulin production and results in dysregulation of blood glucose. People may need to consider insulin variations that can occur throughout the day, their current glucose trend, and the activity context under which an insulin dose is being taken (e.g., prior to exercise, during an illness, etc.) This is difficult for people using MDI therapy, as compared to a person using a pump with a bolus calculator, more recent smart insulin pens have recently made bolus calculation possible for MDI users [4]. For people using CSII pump therapy, a DSS can provide guidance on the basal rate of fast-acting insulin for different time windows during the day, as well as boluses related to meals or hypo and hyperglycemic excursions. Developed DSSs are oftentimes closely integrated with CGM sensors, which provide near real-time (typically every 5 min) estimates of interstitial glucose. We will discuss both short-term glucose prediction algorithms (i.e., 30–60 min in the future), algorithms that predict glucose during and following exercise, and algorithms that predict glucose overnight, prior to bedtime when hypoglycemia can be dangerous
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