Artificial Intelligence in Decision Support Systems for Type 1 Diabetes

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2020 Jun 11

PMID 32517068

Citations 23

Authors

Nichole S Tyler

Peter G Jacobs

Affiliations

Soon will be listed here.

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.

Citing Articles

Impact of machine learning on dietary and exercise behaviors in type 2 diabetes self-management: a systematic literature review.

Mir R, Ul Haq N, Ishaq K, Safie N, Dogar A PeerJ Comput Sci. 2025; 11:e2568.

PMID: 40062277 PMC: 11888848. DOI: 10.7717/peerj-cs.2568.

Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App.

Herrero P, Andorra M, Babion N, Bos H, Koehler M, Klopfenstein Y J Diabetes Sci Technol. 2024; 18(5):1014-1026.

PMID: 39158994 PMC: 11418465. DOI: 10.1177/19322968241267818.

Design and Evaluation of an Exercise Decision Support System Using Digital Twin Models.

Young G, Dodier R, Youssef J, Castle J, Wilson L, Riddell M J Diabetes Sci Technol. 2024; 18(2):324-334.

PMID: 38390855 PMC: 10973845. DOI: 10.1177/19322968231223217.

Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review.

Huang S, Liang Y, Li J, Li X J Med Internet Res. 2023; 25:e51024.

PMID: 38064249 PMC: 10746969. DOI: 10.2196/51024.

Transform diabetes care with precision medicine.

Kannan S, Chellappan D, Kow C, Ramachandram D, Pandey M, Mayuren J Health Sci Rep. 2023; 6(11):e1642.

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