ArticleEClinicalMedicine2022
Development and validation of a machine learning model for classification of next glucose measurement in hospitalized patients.
Article in EClinicalMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 15 citations in OpenAlex.
- Development and prospective evaluation of a real-time deep learning model for inpatient hypoglycemia prediction.NPJ digital medicine · 2026Article
- Accurate prediction of hypoglycemia and hyperglycemia using machine learning in critically ill patients.Scientific reports · 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
- Enhancing glucose level prediction of ICU patients through hierarchical modeling of irregular time-series.Computational and structural biotechnology journal · 2025Article
- Machine learning-based online prediction of nocturnal hypoglycemia in elderly patients with type 2 diabetes.Frontiers in endocrinology · 2025Article
- Development and Validation of Inpatient Hypoglycemia Models Centered Around the Insulin Ordering Process.Journal of diabetes science and technology · 2024Article
- Subtle variation in sepsis-III definitions markedly influences predictive performance within and across methods.Scientific reports · 2024Article
- Prediction of Next Glucose Measurement in Hospitalized Patients by Comparing Various Regression Methods: Retrospective Cohort Study.JMIR formative research · 2023Article
- Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes.Journal of diabetes science and technology · 2023Article
- Hospital Diabetes Meeting 2022.Journal of diabetes science and technology · 2022Article
- Machine Learning Models for Inpatient Glucose Prediction.Current diabetes reports · 2022Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundInpatient glucose management can be challenging due to evolving factors that influence a patient's blood glucose (BG) throughout hospital admission. The purpose of our study was to predict the category of a patient's next BG measurement based on electronic medical record (EMR) data.
methodsEMR data from 184,361 admissions containing 4,538,418 BG measurements from five hospitals in the Johns Hopkins Health System were collected from patients who were discharged between January 1, 2015 and May 31, 2019. Index BGs used for prediction included the 5th to penultimate BG measurements (
findingsIn our internal validation test set, 72·8%, 25·7%, and 1·5% of BG measurements occurring after the index BG were controlled, hyperglycemic, and hypoglycemic respectively. The sensitivity/specificity for prediction of controlled, hyperglycemic, and hypoglycemic were 0·77/0·81, 0·77/0·89, and 0·73/0·91, respectively. On external validation in four hospitals, the ranges of sensitivity/specificity for prediction of controlled, hyperglycemic, and hypoglycemic were 0·64-0·70/0·80-0·87, 0·75-0·80/0·82-0·84, and 0·76-0·78/0·87-0·90, respectively.
interpretationA machine learning algorithm using EMR data can accurately predict the category of a hospitalized patient's next BG measurement. Further studies should determine the effectiveness of integration of this model into the EMR in reducing rates of hypoglycemia and hyperglycemia.
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