ArticleJournal of diabetes science and technology2024
Explainable Machine Learning for Real-Time Hypoglycemia and Hyperglycemia Prediction and Personalized Control Recommendations.
Article in Journal of diabetes science and technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 56 citations in OpenAlex.
- Exploring the Applications of Explainability in Wearable Data Analytics: Systematic Literature Review.Journal of medical Internet research · 2024Pooled it
- Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.Frontiers in public health · 2023Pooled it
- Glycaemic penalty index as an independent predictor of healthcare-associated infection in surgical inpatients: A nested cohort study from the STOIC-D surgery trial.Diabetic medicine : a journal of the British Diabetic Association · 2026Trial
- Updating the Medical History for the Age of Medical Devices.Mayo Clinic proceedings. Innovations, quality & outcomes · 2026Article
- Digital Management of Early-Onset Type 2 Diabetes: Empowerment, Challenges, and Future Outlook.Current diabetes reports · 2026Review
- Development and prospective evaluation of a real-time deep learning model for inpatient hypoglycemia prediction.NPJ digital medicine · 2026Article
- Temporal and Behaviour-Aware Multimodal Modelling for Hour-Ahead Hypoglycaemia Prediction During Ramadan Fasting in Type 1 Diabetes.Sensors (Basel, Switzerland) · 2026Article
- Integrating Pharmacists into CGM-Enabled Digital Diabetes Care: Advancing Personalized and Data-Driven Management.Healthcare (Basel, Switzerland) · 2026Review
- How data drift impacts the safety and interpretability of machine learning models predicting risk from blood glucose control.BMJ digital health & AI · 2026Article
- Interpretable glucose forecasting for type 2 diabetes across traditional, deep, and large language models.Scientific reports · 2025Article
- Can Glucose Alarm Fatigue Threaten the Absolute Clinical Benefit of Continuous Glucose Monitoring in Optimal Glucose Management in Children and Adolescents with Type 1 Diabetes? A Narrative Review.Children (Basel, Switzerland) · 2025Review
- POC Sensor Systems and Artificial Intelligence-Where We Are Now and Where We Are Going?Biosensors · 2025Review
- Explainable cluster-based learning for prediction of postprandial glycemic events and insulin dose optimization in type 1 diabetes.PLOS digital health · 2025Article
- Development of a Prediction Model for Severe Hypoglycemia in Children and Adolescents with Type 1 Diabetes: The Epi-GLUREDIA Study.Nutrients · 2025Article
- Trust in Medical AI: The Case of mHealth Diabetes Apps.Journal of evaluation in clinical practice · 2025Article
- Predictive factors of hypoglycemia in type 2 diabetes: a prospective study using machine learning.Scientific reports · 2025Article
- Death risk prediction model for patients with non-traumatic intracerebral hemorrhage.BMC medical informatics and decision making · 2025Article
- Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025Review
- Machine learning-based prediction of hypoglycemia severity in hospitalized diabetic patients.Frontiers in endocrinology · 2025Article
- Machine Learning-Based Classification of Diabetes: Model Accuracy, Feature Importance, and Clinical Implications.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2025Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe occurrences of acute complications arising from hypoglycemia and hyperglycemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time glucose readings enabling users to manage their control proactively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual's longer term control.
methodsWe introduce explainable machine learning to make predictions of hypoglycemia (<70 mg/dL) and hyperglycemia (>270 mg/dL) up to 60 minutes ahead of time. We train our models using CGM data from 153 people living with T1D in the CITY (CGM Intervention in Teens and Young Adults With Type 1 Diabetes)survey totaling more than 28 000 days of usage, which we summarize into (short-term, medium-term, and long-term) glucose control features along with demographic information. We use machine learning explanations (SHAP [SHapley Additive exPlanations]) to identify which features have been most important in predicting risk per user.
resultsMachine learning models (XGBoost) show excellent performance at predicting hypoglycemia (area under the receiver operating curve [AUROC]: 0.998, average precision: 0.953) and hyperglycemia (AUROC: 0.989, average precision: 0.931) in comparison with a baseline heuristic and logistic regression model.
conclusionsMaximizing model performance for glucose risk prediction and management is crucial to reduce the burden of alarm fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison with baseline models. SHAP helps identify what about a CGM user's glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications.
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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.