ArticleSensors (Basel, Switzerland)2021
Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- 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
- Glucose forecasting and hypoglycemia forewarning in type 1 and type 2 diabetes using deep learning.iScience · 2026Article
- Future-aware blood glucose forecasting using knowledge distillation with transformer-based sequence-to-sequence models.Scientific reports · 2026Article
- Noninvasive Continuous Glucose Monitoring Using Multimodal Near-Infrared, Temperature, and Pressure Signals on the Earlobe.Biosensors · 2025Article
- Intelligent glucose management in hospitalized patients: Short-term glucose and adverse events prediction.PloS one · 2025Article
- Shortcomings in the Evaluation of Blood Glucose Forecasting.IEEE transactions on bio-medical engineering · 2024Article
- Hybrid CNN-GRU Model for Real-Time Blood Glucose Forecasting: Enhancing IoT-Based Diabetes Management with AI.Sensors (Basel, Switzerland) · 2024Article
- HUPA-UCM diabetes dataset.Data in brief · 2024Article
- Article
- Blood Glucose Level Time Series Forecasting: Nested Deep Ensemble Learning Lag Fusion.Bioengineering (Basel, Switzerland) · 2023Article
- Blood glucose forecasting from temporal and static information in children with T1D.Frontiers in pediatrics · 2023Article
- Commercial and Scientific Solutions for Blood Glucose Monitoring-A Review.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
This article proposes two ensemble neural network-based models for blood glucose prediction at three different prediction horizons-30, 60, and 120 min-and compares their performance with ten recently proposed neural networks. The twelve models' performances are evaluated under the same OhioT1DM Dataset, preprocessing workflow, and tools at the three prediction horizons using the most common metrics in blood glucose prediction, and we rank the best-performing ones using three methods devised for the statistical comparison of the performance of multiple algorithms: scmamp, model confidence set, and superior predictive ability. Our analysis provides a comparison of the state-of-the-art neural networks for blood glucose prediction, estimating the model's error, highlighting those with the highest probability of being the best predictors, and providing a guide for their use in clinical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.