ReviewSensors (Basel, Switzerland)2021
Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges.
Review 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 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.Frontiers in public health · 2023Pooled it
- Development and prospective evaluation of a real-time deep learning model for inpatient hypoglycemia prediction.NPJ digital medicine · 2026Article
- Personalized Type 1 Diabetes Management: Reinforcement Learning-Based Insulin Dosing and Glucose Forecasting.JMIR diabetes · 2026Article
- Artificial Intelligence for Blood Glucose Level Prediction in Type 1 Diabetes: Methods, Evaluation, and Emerging Advances.Sensors (Basel, Switzerland) · 2026Review
- Double-Layer Simplified Complex Interval Neural Network Stacking for Blood Glucose Prediction of Continuous Glucose Monitoring System.Biosensors · 2025Article
- Managing Exercise-Related Glycemic Events in Type 1 Diabetes: Development and Validation of Predictive Models for a Practical Decision Support Tool.JMIR diabetes · 2025Article
- Development of a Prediction Model for Severe Hypoglycemia in Children and Adolescents with Type 1 Diabetes: The Epi-GLUREDIA Study.Nutrients · 2025Article
- Transparency and Validity of Artificial Intelligence Applications in Pediatric Diabetes: A Systematic Review.Cureus · 2025Review
- Enhancing glucose level prediction of ICU patients through hierarchical modeling of irregular time-series.Computational and structural biotechnology journal · 2025Article
- A prior-knowledge-guided dynamic attention mechanism to predict nocturnal hypoglycemic events in type 1 diabetes.BMC medical informatics and decision making · 2024Article
- Predicting Dysglycemia in Patients with Diabetes Using Electrocardiogram.Diagnostics (Basel, Switzerland) · 2024Article
- Machine Learning Algorithms Based on Time Series Pre-Clustering for Nocturnal Glucose Prediction in People with Type 1 Diabetes.Diagnostics (Basel, Switzerland) · 2024Article
- Enhancing severe hypoglycemia prediction in type 2 diabetes mellitus through multi-view co-training machine learning model for imbalanced dataset.Scientific reports · 2024Article
- Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App.Journal of diabetes science and technology · 2024Article
- Explainable hypoglycemia prediction models through dynamic structured grammatical evolution.Scientific reports · 2024Article
- Non-invasive glucose prediction and classification using NIR technology with machine learning.Heliyon · 2024Article
- Article
- Machine Learning and Deep Learning Models for Nocturnal High- and Low-Glucose Prediction in Adults with Type 1 Diabetes.Diagnostics (Basel, Switzerland) · 2024Article
- Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges.Diabetologia · 2024Review
- Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes.IEEE reviews in biomedical engineering · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, and in extreme cases, death. In almost half of all the severe cases, hypoglycemia arrives unannounced and is essentially asymptomatic. The inability of a diabetic patient to anticipate and intervene the occurrence of a hypoglycemic event often results in crisis. Hence, the prediction of hypoglycemia is a vital step in improving the life quality of a diabetic patient. The objective of this paper is to review work performed in the domain of hypoglycemia prediction by using machine learning and also to explore the latest trends and challenges that the researchers face in this area; (2) Methods: literature obtained from PubMed and Google Scholar was reviewed. Manuscripts from the last five years were searched for this purpose. A total of 903 papers were initially selected of which 57 papers were eventually shortlisted for detailed review; (3) Results: a thorough dissection of the shortlisted manuscripts provided an interesting split between the works based on two categories: hypoglycemia prediction and hypoglycemia detection. The entire review was carried out keeping this categorical distinction in perspective while providing a thorough overview of the machine learning approaches used to anticipate hypoglycemia, the type of training data, and the prediction horizon.
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What Socratic holds
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 Socratic graph.