Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning
Open accessAbstract
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.
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Although each module of Vehì et al.'s Patient Safety System has been independently validated on retrospective data, a clinical validation of the proposed DSS is needed to prove its efficacy as a tool for improving glycemic control in subjects with T1D.
“Although each module has been independently validated on retrospective data, a clinical validation of the proposed DSS is needed to prove its efficacy as a tool for improving glycemic control in subjects with T1D.”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [61]
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Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors
Vettoretti, M., Cappon, G., Facchinetti, A., & Sparacino, G. (2020) · Sensors · doi:10.3390/s20143870 · Cited by 176 · see citing works
Hypoglycaemia and its management in primary care setting
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- Authors
- Vehí, J., Contreras, I., Oviedo, S., Biagi, L., & Bertachi, A.
- Affiliations
- Universitat de Girona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Spain
- Universitat de Girona (ES)
- Universidade Tecnológica Federal do Paraná (BR)
- Venue
- Health Informatics Journal
- Publisher
- SAGE Publications
- Published
- 2019-06-13
- Volume
- 26
- Issue
- 1
- Pages
- 703-718
- ISSN
- 1355-4743 (ISSN-L)
- 1460-4582
- 1741-2811
- OpenAlex
- W2949554208
- Semantic Scholar
- c01c050ced45bf95b70de0c7b5d84ba4bd46fac9
- Cited by
- 76
- References
- 47
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 13 · 2023: 11 · 2022: 16 · 2021: 12 · 2020: 9
- FWCI
- 7.53
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Health Informatics Journal (publishedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministerio de Ciencia e Innovación (DPI2016-78831-C2-2-R)
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (202050/2015-7, 207688/2014-1)
- Ministerio de Educación, Cultura y Deporte (ES-2014-068289)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- type 1 diabetes, hypoglycemia prevention, intensive insulin therapy, blood glucose prediction, grammatical evolution, support vector machine, artificial neural networks, data mining, decision support system, patient safety
- MeSH terms
- Machine Learning, Diabetes Mellitus, Type 1, Humans, Hypoglycemia, Hypoglycemic Agents, Quality of Life
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