ReviewCurrent diabetes reports2022
Machine Learning Models for Inpatient Glucose Prediction.
Review in Current diabetes reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.Frontiers in public health · 2023Pooled it
- Algorithm-guided insulin therapy in hospitalized patients: current evidence, implementation challenges, and future perspectives.Reviews in endocrine & metabolic disorders · 2026Review
- Tailoring interventions to close gaps in diabetes mellitus care.Nature reviews. Endocrinology · 2026Review
- Extracellular Vesicles in Gestational Diabetes Mellitus: Pathogenesis, Diagnosis, and Therapy.International journal of nanomedicine · 2026Review
- Accurate prediction of hypoglycemia and hyperglycemia using machine learning in critically ill patients.Scientific reports · 2025Article
- Comparison of logistic regression and artificial neural network models for predicting hypoglycemia in non-ICU inpatients with diabetes.BMC endocrine disorders · 2025Article
- A Machine-Learning-Based Clinical Decision Model for Predicting Amputation Risk in Patients with Diabetic Foot Ulcers: Diagnostic Performance and Practical Implications.Diagnostics (Basel, Switzerland) · 2025Article
- Development and validation of a machine learning model for real-time blood glucose prediction for ICU patients.BMC medical informatics and decision making · 2025Article
- Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes.NPJ digital medicine · 2025Article
- Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin ManagementJournal of clinical research in pediatric endocrinology · 2025Article
- A distributional reinforcement learning model for optimal glucose control after cardiac surgery.NPJ digital medicine · 2025Article
- Cyclic dual latent discovery for improved blood glucose prediction through patient-provider interaction modeling: a prediction study.Ewha medical journal · 2025Article
- 16. Diabetes Care in the Hospital: Standards of Care in Diabetes-2025.Diabetes care · 2025Review
- Enhancing glucose level prediction of ICU patients through hierarchical modeling of irregular time-series.Computational and structural biotechnology journal · 2025Article
- Development of machine learning models for predicting postoperative hyperglycemia in non-diabetic gastric cancer patients: a retrospective cohort study analysis.Frontiers in endocrinology · 2025Article
- Development and validation of machine-learning models of diet management for hyperphenylalaninemia: a multicenter retrospective study.BMC medicine · 2024Observational
- Glu-Ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients.Heliyon · 2024Article
- Development and Validation of Inpatient Hypoglycemia Models Centered Around the Insulin Ordering Process.Journal of diabetes science and technology · 2024Article
- Prediction of Next Glucose Measurement in Hospitalized Patients by Comparing Various Regression Methods: Retrospective Cohort Study.JMIR formative research · 2023Article
- Heterogeneous temporal representation for diabetic blood glucose prediction.Frontiers in physiology · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewGlucose management in the hospital is difficult due to non-static factors such as antihyperglycemic and steroid doses, renal function, infection, surgical status, and diet. Given these complex and dynamic factors, machine learning approaches can be leveraged for prediction of glucose trends in the hospital to mitigate and prevent suboptimal hypoglycemic and hyperglycemic outcomes. Our aim was to review the clinical evidence for the role of machine learning-based models in predicting hospitalized patients' glucose trajectory. RECENT
findingsThe published literature on machine learning algorithms has varied in terms of population studied, outcomes of interest, and validation methods. There have been tools developed that utilize data from both continuous glucose monitors and large electronic health records (EHRs). With increasing sample sizes, inclusion of a greater number of predictor variables, and use of more advanced machine learning algorithms, there has been a trend in recent years towards increasing predictive accuracy for glycemic outcomes in the hospital setting. While current models predicting glucose trajectory offer promising results, they have not been tested prospectively in the clinical setting. Accurate machine learning algorithms have been developed and validated for prediction of hypoglycemia and hyperglycemia in the hospital. Further work is needed in implementation/integration of machine learning models into EHR systems, with prospective studies to evaluate effectiveness and safety of such clinical decision support on glycemic and other clinical outcomes.
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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.