ArticleJournal of personalized medicine2022
Machine Learning Models for Nocturnal Hypoglycemia Prediction in Hospitalized Patients with Type 1 Diabetes.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.Frontiers in public health · 2023Pooled it
- Evaluation of a Prediction-Based Bedtime Intervention in Reducing Nocturnal Low Glucose in Adults With Type 1 Diabetes: The DailyDose Bedtime Smart Snack Crossover Study.Diabetes care · 2025Trial
- Temporal and Behaviour-Aware Multimodal Modelling for Hour-Ahead Hypoglycaemia Prediction During Ramadan Fasting in Type 1 Diabetes.Sensors (Basel, Switzerland) · 2026Article
- Transforming hypoglycemia prediction in adult type 1 diabetes: a systematic review and meta-analysis for precision care.Open life sciences · 2026Article
- Development and validation of an interpretable machine learning model for predicting in-hospital hypoglycemia in adults with type 1 diabetes mellitus: a multicenter retrospective study.Frontiers in endocrinology · 2026Article
- Comparative machine learning models for hypertension prediction in a cohort of patients with diabetes using routine clinical variables.Archives of endocrinology and metabolism · 2025Article
- Enhancing glucose level prediction of ICU patients through hierarchical modeling of irregular time-series.Computational and structural biotechnology journal · 2025Article
- Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025Review
- Machine Learning Algorithms Based on Time Series Pre-Clustering for Nocturnal Glucose Prediction in People with Type 1 Diabetes.Diagnostics (Basel, Switzerland) · 2024Article
- Machine Learning and Deep Learning Models for Nocturnal High- and Low-Glucose Prediction in Adults with Type 1 Diabetes.Diagnostics (Basel, Switzerland) · 2024Article
- Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case-control study.BMJ open diabetes research & care · 2024Article
- Predicting risk for nocturnal hypoglycemia after physical activity in children with type 1 diabetes.Frontiers in medicine · 2024Article
- Combining uncertainty-aware predictive modeling and a bedtime Smart Snack intervention to prevent nocturnal hypoglycemia in people with type 1 diabetes on multiple daily injections.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Long-Term Glucose Forecasting for Open-Source Automated Insulin Delivery Systems: A Machine Learning Study with Real-World Variability Analysis.Healthcare (Basel, Switzerland) · 2023Article
- Prediction of gastrointestinal cancers in the ONCONUT cohort study: comparison between logistic regression and artificial neural network.Frontiers in oncology · 2023Article
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Nocturnal hypoglycemia (NH) is a dangerous complication of insulin therapy that often goes undetected. In this study, we aimed to generate machine learning (ML)-based models for short-term NH prediction in hospitalized patients with type 1 diabetes (T1D). The models were trained on continuous glucose monitoring (CGM) data obtained from 406 adult patients admitted to a tertiary referral hospital. Eight CGM-derived metrics of glycemic control and glucose variability were included in the models. Combinations of CGM and clinical data (23 parameters) were also assessed. Random Forest (RF), Logistic Linear Regression with Lasso regularization, and Artificial Neuron Networks algorithms were applied. In our models, RF provided the best prediction accuracy with 15 min and 30 min prediction horizons. The addition of clinical parameters slightly improved the prediction accuracy of most models, whereas oversampling and undersampling procedures did not have significant effects. The areas under the curve of the best models based on CGM and clinical data with 15 min and 30 min prediction horizons were 0.97 and 0.942, respectively. Basal insulin dose, diabetes duration, proteinuria, and HbA1c were the most important clinical predictors of NH assessed by RF. In conclusion, ML is a promising approach to personalized prediction of NH in hospitalized patients with T1D.
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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.