ArticleJournal of diabetes science and technology2023
Long-Term Prediction of Blood Glucose Levels in Type 1 Diabetes Using a CNN-LSTM-Based Deep Neural Network.
Article in Journal of diabetes science and technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Trustworthy AI for personalized glycemic control: a systematic review and critical appraisal of multimodal forecasting and safety-critical closed-loop control.Reviews in endocrine & metabolic disorders · 2026Review
- Article
- Future-aware blood glucose forecasting using knowledge distillation with transformer-based sequence-to-sequence models.Scientific reports · 2026Article
- Artificial intelligence-driven therapeutics for disease modification in type 1 diabetes: a digital public health and clinical translation framework.Frontiers in public health · 2026Review
- A personalized federated learning-based glucose prediction algorithm for high-risk glycemic excursion regions in type 1 diabetes.Scientific reports · 2025Article
- Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin ManagementJournal of clinical research in pediatric endocrinology · 2025Article
- Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes.PLOS digital health · 2025Article
- A Comparative Study of Transformer-Based Models for Multi-Horizon Blood Glucose Prediction.ArXiv · 2025Article
- Personalized Blood Glucose Forecasting From Limited CGM Data Using Incrementally Retrained LSTM.IEEE transactions on bio-medical engineering · 2025Article
- Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling.BMC medical informatics and decision making · 2025Observational
- Towards a decision support system for post bariatric hypoglycaemia: development of forecasting algorithms in unrestricted daily-life conditions.BMC medical informatics and decision making · 2025Article
- Deep learning and generative artificial intelligence in aging research and healthy longevity medicine.Aging · 2025Review
- Harnessing gut-derived bioactives and AI diagnostics for the next generation of type 2 diabetes solutions.Frontiers in endocrinology · 2025Review
- Shortcomings in the Evaluation of Blood Glucose Forecasting.IEEE transactions on bio-medical engineering · 2024Article
- Beyond Expected Patterns in Insulin Needs of People With Type 1 Diabetes: Temporal Analysis of Automated Insulin Delivery Data.JMIRx med · 2024Article
- Predicting Neonatal Hypoglycemia Using AI Neural Networks in Infants from Mothers with Gestational Diabetes Mellitus.Medical science monitor : international medical journal of experimental and clinical research · 2024Article
- Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks.Biomedical optics express · 2024Article
- Glu-Ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients.Heliyon · 2024Article
- Machine Learning and Deep Learning Models for Nocturnal High- and Low-Glucose Prediction in Adults with Type 1 Diabetes.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn this work, we leverage state-of-the-art deep learning-based algorithms for blood glucose (BG) forecasting in people with type 1 diabetes.
methodsWe propose stacks of convolutional neural network and long short-term memory units to predict BG level for 30-, 60-, and 90-minute prediction horizon (PH), given historical glucose measurements, meal information, and insulin intakes. The evaluation was performed on two data sets, Replace-BG and DIAdvisor, representative of free-living conditions and in-hospital setting, respectively.
resultsFor 90-minute PH, our model obtained mean absolute error of 17.30 ± 2.07 and 18.23 ± 2.97 mg/dL, root mean square error of 23.45 ± 3.18 and 25.12 ± 4.65 mg/dL, coefficient of determination of 84.13 ± 4.22% and 82.34 ± 4.54%, and in terms of the continuous glucose-error grid analysis 94.71 ± 3.89% and 91.71 ± 4.32% accurate predictions, 1.81 ± 1.06% and 2.51 ± 0.86% benign errors, and 3.47 ± 1.12% and 5.78 ± 1.72% erroneous predictions, for Replace-BG and DIAdvisor data sets, respectively.
conclusionOur investigation demonstrated that our method achieved superior glucose forecasting compared with existing approaches in the literature, and thanks to its generalizability showed potential for real-life applications.
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