ArticleJournal of diabetes science and technology2021
Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction.
Article in Journal of diabetes science and technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 54 papers, 4 of them syntheses that pooled it.
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
54 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic 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
- Machine Learning and Smart Devices for Diabetes Management: Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Telemedicine in pediatrics: a critical narrative review of innovations, limitations, and future priorities.European journal of pediatrics · 2026Review
- Development and prospective evaluation of a real-time deep learning model for inpatient hypoglycemia prediction.NPJ digital medicine · 2026Article
- Summarizing data from continuous glucose monitors using the cgmstats command.The Stata journal · 2026Article
- Artificial Intelligence for Blood Glucose Level Prediction in Type 1 Diabetes: Methods, Evaluation, and Emerging Advances.Sensors (Basel, Switzerland) · 2026Review
- Summarizing data from continuous glucose monitors using the cgmstats package.medRxiv : the preprint server for health sciences · 2026Article
- Machine learning risk prediction models for medication harm in hospitalised adult patients.Therapeutic advances in drug safety · 2026Article
- How data drift impacts the safety and interpretability of machine learning models predicting risk from blood glucose control.BMJ digital health & AI · 2026Article
- A personalized federated learning-based glucose prediction algorithm for high-risk glycemic excursion regions in type 1 diabetes.Scientific reports · 2025Article
- Event-triggered smart dual hormone artificial pancreas for patient-specific drug delivery.Scientific reports · 2025Article
- 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
- Transparency and Validity of Artificial Intelligence Applications in Pediatric Diabetes: A Systematic Review.Cureus · 2025Review
- Chronobiologically-informed features from CGM data provide unique information for XGBoost prediction of longer-term glycemic dysregulation in 8,000 individuals with type-2 diabetes.PLOS digital health · 2025Article
- Machine Learning in Pediatric Healthcare: Current Trends, Challenges, and Future Directions.Journal of clinical medicine · 2025Review
- Real-World Insights Into Hypoglycemia Risk While Driving in Teens and Young Adults With Type 1 Diabetes.Pediatric diabetes · 2025Article
- Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025Review
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
backgroundHypoglycemia is a serious health concern in youth with type 1 diabetes (T1D). Real-time data from continuous glucose monitoring (CGM) can be used to predict hypoglycemic risk, allowing patients to take timely intervention measures.
methodsA machine learning model is developed for probabilistic prediction of hypoglycemia (<70 mg/dL) in 30- and 60-minute time horizons based on CGM datasets obtained from 112 patients over a range of 90 days consisting of over 1.6 million CGM values under normal living conditions. A comprehensive set of features relevant for hypoglycemia are developed and a parsimonious subset with most influence on predicting hypoglycemic risk is identified. Model performance is evaluated both with and without contextual information on insulin and carbohydrate intake.
resultsThe model predicted hypoglycemia with >91% sensitivity for 30- and 60-minute prediction horizons while maintaining specificity >90%. Inclusion of insulin and carbohydrate data yielded performance improvement for 60-minute but not for 30-minute predictions. Model performance was highest for nocturnal hypoglycemia (~95% sensitivity). Shortterm (less than one hour) and medium-term (one to four hours) features for good prediction performance are identified.
conclusionsInnovative feature identification facilitated high performance for hypoglycemia risk prediction in pediatric youth with T1D. Timely alerts of impending hypoglycemia may enable proactive measures to avoid severe hypoglycemia and achieve optimal glycemic control. The model will be deployed on a patient-facing smartphone application in an upcoming pilot study.
Indexed as
Identifiers
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.