Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning

Created by W.Langdon from gp-bibliography.bib Revision:1.8592

@Article{Vehi:2020:jhi,

• author = "Josep Vehi and Ivan Contreras and Silvia Oviedo and Lyvia Biagi and Arthur Bertachi",
• title = "Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning",
• journal = "Health Informatics Journal",
• year = "2020",
• volume = "26",
• number = "1",
• pages = "703--718",
• month = mar,
• keywords = "genetic algorithms, genetic programming, grammatical evolution, blood glucose prediction, decision support systems, diabetes management, hypoglycaemia, machine learning, type-1 diabetes",
• ISSN = "1460-4582",
• URL = "https://pubmed.ncbi.nlm.nih.gov/31195880/",
• URL = "https://journals.sagepub.com/doi/full/10.1177/1460458219850682",
• DOI = "doi:10.1177/1460458219850682",
• size = "16 pages",
• abstract = "Tight blood glucose control reduces the risk of microvascular and macrovascular complications in patients with type 1 diabetes. However, this is very difficult due to the large intra-individual variability and other factors that affect glycaemic control. The main limiting factor to achieve strict control of glucose levels in patients on intensive insulin therapy is the risk of severe hypoglycaemia. Therefore, hypoglycaemia is the main safety problem in the treatment of type 1 diabetes, negatively affecting the quality of life of patients suffering from this disease. Decision support tools based on machine learning methods have become a viable way to enhance patient safety by anticipating adverse glycaemic events. This study proposes the application of four machine learning algorithms to tackle the problem of safety in diabetes management: (1) grammatical evolution for the mid-term continuous prediction of blood glucose levels, (2) support vector machines to predict hypoglycaemic events during postprandial periods, (3) artificial neural networks to predict hypoglycaemic episodes overnight, and (4) data mining to profile diabetes management scenarios. The proposal consists of the combination of prediction and classification capabilities of the implemented approaches. The resulting system significantly reduces the number of episodes of hypoglycaemia, improving safety and providing patients with greater confidence in decision-making",
• notes = "Universitat de Girona, Spain; Centro de Investigacion Biomedica en Red de Diabetes y Enfermedades Metabolicas Asociadas (CIBERDEM), Spain

  journals.sagepub.com/home/jhi",

}

Genetic Programming entries for Josep Vehi Ivan Contreras Silvia Oviedo Castillo Lyvia Biagi Arthur Bertachi

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