ArticlePloS one2017
Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments.Diabetes/metabolism research and reviews · 2021Pooled it
- Recent Advances in Modeling and Prediction of Blood Glucose in Type 1 Diabetes.Delaware journal of public health · 2026Article
- Proliferative Diabetic Retinopathy in Young-Onset Type 1 Diabetes in Croatia: Risk Factors and a Predictive Economic Model for National Screening.Medicina (Kaunas, Lithuania) · 2025Article
- Personalized Model Identification for Glucose Dynamics From Clinical Data With Incomplete Inputs.IEEE transactions on bio-medical engineering · 2025Article
- A perspective on harmonizing diabetes management datasets.Data in brief · 2025Article
- Cyber-Physical-Human Systems in Precision Medicine: Advances in Artificial Pancreas for Treatment of Diabetes.Annual reviews in control · 2025Article
- Simulating macroevolutionary trends and open-ended evolution with a novel mechanistic multi-level approach.PloS one · 2025Article
- Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes.IEEE reviews in biomedical engineering · 2024Review
- Performance Effect of Adjusting Insulin Sensitivity for Model-Based Automated Insulin Delivery Systems.Journal of diabetes science and technology · 2023Article
- Chinese diabetes datasets for data-driven machine learning.Scientific data · 2023Article
- GA-MADRID: design and validation of a machine learning tool for the diagnosis of Alzheimer's disease and frontotemporal dementia using genetic algorithms.Medical & biological engineering & computing · 2022Article
- Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models.Sensors (Basel, Switzerland) · 2022Article
- Quantifying the impact of physical activity on future glucose trends using machine learning.iScience · 2022Article
- A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin.Sensors (Basel, Switzerland) · 2022Article
- Recent Advances in Computational Modeling of Biomechanics and Biorheology of Red Blood Cells in Diabetes.Biomimetics (Basel, Switzerland) · 2022Article
- GLYFE: review and benchmark of personalized glucose predictive models in type 1 diabetes.Medical & biological engineering & computing · 2022Review
- Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients.NPJ digital medicine · 2021Article
- Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction.Journal of diabetes science and technology · 2021Article
- Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study.JMIR diabetes · 2021Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The large patient variability in human physiology and the effects of variables such as exercise or meals challenge current prediction modeling techniques. Physiological models are very precise but they are typically complex and specific physiological knowledge is required. In contrast, data-based models allow the incorporation of additional inputs and accurately capture the relationship between these inputs and the outcome, but at the cost of losing the physiological meaning of the model. In this work, we designed a hybrid approach comprising physiological models for insulin and grammatical evolution, taking into account the clinical harm caused by deviations from the target blood glucose by using a penalizing fitness function based on the Clarke error grid. The prediction models were built using data obtained over 14 days for 100 virtual patients generated by the UVA/Padova T1D simulator. Midterm blood glucose was predicted for the 100 virtual patients using personalized models and different scenarios. The results obtained were promising; an average of 98.31% of the predictions fell in zones A and B of the Clarke error grid. Midterm predictions using personalized models are feasible when the configuration of grammatical evolution explored in this study is used. The study of new alternative models is important to move forward in the development of alarm-and-control applications for the management of type 1 diabetes and the customization of the patient's treatments. The hybrid approach can be adapted to predict short-term blood glucose values to detect continuous glucose-monitoring sensor errors and to estimate blood glucose values when the continuous glucose-monitoring system fails to provide them.
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