Data-based algorithms and models using diabetics real data for blood glucose and hypoglycaemia prediction – A systematic literature review

• Published in: Artificial intelligence in medicine Vol. 118; p. 102120
• Main Authors: Felizardo, Virginie, Garcia, Nuno M., Pombo, Nuno, Megdiche, Imen
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.08.2021; Elsevier
• Subjects: Algorithms; Artificial Intelligence; Blood Glucose; Blood glucose level; Computer Science; Data-based algorithms or models; Databases, Factual; Diabetes Mellitus - diagnosis; Diabetes Mellitus - epidemiology; Diabetics real data; Humans; Hypoglycaemia or hypoglycemia; Hypoglycemia - chemically induced; Hypoglycemia - diagnosis; Hypoglycemia - epidemiology; Prediction; Diabetics real data; Hypoglycaemia or hypoglycemia; Blood glucose level; Data-based algorithms or models; Prediction
• ISSN: 0933-3657; 1873-2860; 1873-2860
• DOI: 10.1016/j.artmed.2021.102120

Hypoglycaemia prediction play an important role in diabetes management being able to reduce the number of dangerous situations. Thus, it is relevant to present a systematic review on the currently available prediction algorithms and models for hypoglycaemia (or hypoglycemia in US English) prediction. This study aims to systematically review the literature on data-based algorithms and models using diabetics real data for hypoglycaemia prediction. Five electronic databases were screened for studies published from January 2014 to June 2020: ScienceDirect, IEEE Xplore, ACM Digital Library, SCOPUS, and PubMed. Sixty-three eligible studies were retrieved that met the inclusion criteria. The review identifies the current trend in this topic: most of the studies perform short-term predictions (82.5%). Also, the review pinpoints the inputs and shows that information fusion is relevant for hypoglycaemia prediction. Regarding data-based models (80.9%) and hybrid models (19.1%) different predictive techniques are used: Artificial neural network (22.2%), ensemble learning (27.0%), supervised learning (20.6%), statistic/probabilistic (7.9%), autoregressive (7.9%), evolutionary (6.4%), deep learning (4.8%) and adaptative filter (3.2%). Artificial Neural networks and hybrid models show better results. The data-based models for blood glucose and hypoglycaemia prediction should be able to provide a good balance between the applicability and performance, integrating complementary data from different sources or from different models. This review identifies trends and possible opportunities for research in this topic. •Hypoglycaremia is is commonly feared in patients with insulin therapy;•The hypoglycaemia prediction has been addressed as a blood glucose prediction using continuous glucose monitoring systems;•The importance of data- and model-fusion for the predictions;•The potential of Artificial Intelligence and machine learning on hypoglycaemia predictions;