ArticleJMIR diabetes2021
Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study.
Article in JMIR diabetes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Noninvasive Detection of Acute Hyperglycemia Using Signal from Wearable ECG Sensors Considering Individual HRV Response Delays to Glucose.Biosensors · 2026Article
- Recent Advances in Modeling and Prediction of Blood Glucose in Type 1 Diabetes.Delaware journal of public health · 2026Article
- Deep Learning-Enabled Diabetic Retinopathy Screening: A Techno-Clinical Revolution or Just More Artificial Intelligence Hype?Diabetes care · 2026Article
- A personalized federated learning-based glucose prediction algorithm for high-risk glycemic excursion regions in type 1 diabetes.Scientific reports · 2025Article
- 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
- Real-World Insights Into Hypoglycemia Risk While Driving in Teens and Young Adults With Type 1 Diabetes.Pediatric diabetes · 2025Article
- Nocturnal Hypoglycemia in the Era of Continuous Glucose Monitoring.Journal of diabetes science and technology · 2024Review
- Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App.Journal of diabetes science and technology · 2024Article
- Development and validation of a prediction model for self-reported hypoglycemia risk in patients with type 2 diabetes: A longitudinal cohort study.Journal of diabetes investigation · 2024Article
- Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: Electrocardiograms and Accelerometry.Journal of diabetes science and technology · 2024Article
- Explainable Machine Learning for Real-Time Hypoglycemia and Hyperglycemia Prediction and Personalized Control Recommendations.Journal of diabetes science and technology · 2024Article
- Dementia in Diabetes: The Role of Hypoglycemia.International journal of molecular sciences · 2023Review
- Hypoglycemia Detection Using Hand Tremors: Home Study of Patients With Type 1 Diabetes.JMIR diabetes · 2023Article
- Systemic inflammation as a biomarker of seizure propensity and a target for treatment to reduce seizure propensity.Epilepsia open · 2023Review
- Feature Transformation for Efficient Blood Glucose Prediction in Type 1 Diabetes Mellitus Patients.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
- Type 1 Diabetes Hypoglycemia Prediction Algorithms: Systematic Review.JMIR diabetes · 2022Review
- Development and validation of a machine learning model for classification of next glucose measurement in hospitalized patients.EClinicalMedicine · 2022Article
- Hypoglycemia event prediction from CGM using ensemble learning.Frontiers in clinical diabetes and healthcare · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundPredictive alerts for impending hypoglycemic events enable persons with type 1 diabetes to take preventive actions and avoid serious consequences.
objectiveThis study aimed to develop a prediction model for hypoglycemic events with a low false alert rate, high sensitivity and specificity, and good generalizability to new patients and time periods.
methodsPerformance improvement by focusing on sustained hypoglycemic events, defined as glucose values less than 70 mg/dL for at least 15 minutes, was explored. Two different modeling approaches were considered: (1) a classification-based method to directly predict sustained hypoglycemic events, and (2) a regression-based prediction of glucose at multiple time points in the prediction horizon and subsequent inference of sustained hypoglycemia. To address the generalizability and robustness of the model, two different validation mechanisms were considered: (1) patient-based validation (model performance was evaluated on new patients), and (2) time-based validation (model performance was evaluated on new time periods).
resultsThis study utilized data from 110 patients over 30-90 days comprising 1.6 million continuous glucose monitoring values under normal living conditions. The model accurately predicted sustained events with >97% sensitivity and specificity for both 30- and 60-minute prediction horizons. The false alert rate was kept to <25%. The results were consistent across patient- and time-based validation strategies.
conclusionsProviding alerts focused on sustained events instead of all hypoglycemic events reduces the false alert rate and improves sensitivity and specificity. It also results in models that have better generalizability to new patients and time periods.
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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.