ArticleBMC medical informatics and decision making2019
A machine-learning approach to predict postprandial hypoglycemia.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 71 citations in OpenAlex.
- Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.Frontiers in public health · 2023Pooled it
- Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments.Diabetes/metabolism research and reviews · 2021Pooled it
- Artificial Intelligence for Blood Glucose Level Prediction in Type 1 Diabetes: Methods, Evaluation, and Emerging Advances.Sensors (Basel, Switzerland) · 2026Review
- Temporal and Behaviour-Aware Multimodal Modelling for Hour-Ahead Hypoglycaemia Prediction During Ramadan Fasting in Type 1 Diabetes.Sensors (Basel, Switzerland) · 2026Article
- Construction and validation of a hypoglycemia risk prediction model for hospitalized type 2 diabetes patients based on machine learning.BMC endocrine disorders · 2025Article
- Generalized multi task learning framework for glucose forecasting and hypoglycemia detection using simulation to reality.NPJ digital medicine · 2025Article
- Explainable cluster-based learning for prediction of postprandial glycemic events and insulin dose optimization in type 1 diabetes.PLOS digital health · 2025Article
- Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction.Scientific reports · 2025Article
- Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025Review
- One-year outcomes of a digital twin intervention for type 2 diabetes: a retrospective real-world study.Scientific reports · 2024Observational
- Explainable hypoglycemia prediction models through dynamic structured grammatical evolution.Scientific reports · 2024Article
- Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis.JMIR medical informatics · 2023Article
- Application of Machine Learning Algorithms to Predict Uncontrolled Diabetes Using the All of Us Research Program Data.Healthcare (Basel, Switzerland) · 2023Article
- A lung cancer risk warning model based on tongue images.Frontiers in physiology · 2023Article
- A hybrid sampling algorithm combining synthetic minority over-sampling technique and edited nearest neighbor for missed abortion diagnosis.BMC medical informatics and decision making · 2022Article
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
- Risk-factor model for postpartum hemorrhage after cesarean delivery: a retrospective study based on 3498 patients.Scientific reports · 2022Article
- Hypoglycemia event prediction from CGM using ensemble learning.Frontiers in clinical diabetes and healthcare · 2022Article
- Article
- Quantification of Myocardial Blood Flow by Machine Learning Analysis of Modified Dual Bolus MRI Examination.Annals of biomedical engineering · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFor an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set.
methodsWe use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance.
resultsIn predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models.
conclusionIn conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.
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