ArticleSensors (Basel, Switzerland)2022
A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.Frontiers in public health · 2023Pooled it
- 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
- Transforming hypoglycemia prediction in adult type 1 diabetes: a systematic review and meta-analysis for precision care.Open life sciences · 2026Article
- Artificial Intelligence in Personalized Medicine for Diabetes Mellitus: A Narrative Review.Cureus · 2025Review
- 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
- 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
- Predicting Glucose Values: A New Era for Continuous Glucose Monitoring.Journal of diabetes science and technology · 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
- Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis.JMIR medical informatics · 2023Article
- Managing Severe Hypoglycaemia in Patients with Diabetes: Current Challenges and Emerging Therapies.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Review
- 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
9 authors at 4 institutions in 2 countries.
Funding
Abstract
Nocturnal hypoglycemia (NH) is one of the most challenging events for multiple dose insulin therapy (MDI) in people with type 1 diabetes (T1D). The goal of this study is to design a method to reduce the incidence of NH in people with T1D under MDI therapy, providing a decision-support system and improving confidence toward self-management of the disease considering the dataset used by Bertachi et al. Different machine learning (ML) algorithms, data sources, optimization metrics and mitigation measures to predict and avoid NH events have been studied. In addition, we have designed population and personalized models and studied the generalizability of the models and the influence of physical activity (PA) on them. Obtaining 30 g of rescue carbohydrates (CHO) is the optimal value for preventing NH, so it can be asserted that this is the value with which the time under 70 mg/dL decreases the most, with almost a 35% reduction, while increasing the time in the target range by 1.3%. This study supports the feasibility of using ML techniques to address the prediction of NH in patients with T1D under MDI therapy, using continuous glucose monitoring (CGM) and a PA tracker. The results obtained prove that BG predictions can not only be critical in achieving safer diabetes management, but also assist physicians and patients to make better and safer decisions regarding insulin therapy and their day-to-day lives.
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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 Socratic graph.