ArticleJournal of clinical monitoring and computing2022
Predicting hypoglycemia in critically Ill patients using machine learning and electronic health records.
Article in Journal of clinical monitoring and computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Development and prospective evaluation of a real-time deep learning model for inpatient hypoglycemia prediction.NPJ digital medicine · 2026Article
- Artificial intelligence in intensive care units: a scoping review addressing the translational gap to clinical practice.Korean journal of anesthesiology · 2026Article
- Accurate prediction of hypoglycemia and hyperglycemia using machine learning in critically ill patients.Scientific reports · 2025Article
- Development and validation of a novel nomogram incorporating psychosocial factors for predicting hypoglycemia during perioperative period of digestive endoscopy (DREAM-Hypo Model).BMC gastroenterology · 2025Observational
- Flash Glucose Monitoring for Predicting Cardiogenic Shock Occurrence in Critically Ill Patients: A Retrospective Pilot Study.Diagnostics (Basel, Switzerland) · 2025Article
- Mapping Artificial Intelligence Research Trends in Critical Care Nursing: A Bibliometric Analysis.Journal of multidisciplinary healthcare · 2025Review
- Glucose management in critically ill adults: A qualitative study from the experiences of health care providers.Heliyon · 2024Article
- Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes.Journal of diabetes science and technology · 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
- Hospital Diabetes Meeting 2022.Journal of diabetes science and technology · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Hypoglycemia is a common occurrence in critically ill patients and is associated with significant mortality and morbidity. We developed a machine learning model to predict hypoglycemia by using a multicenter intensive care unit (ICU) electronic health record dataset. Machine learning algorithms were trained and tested on patient data from the publicly available eICU Collaborative Research Database. Forty-four features including patient demographics, laboratory test results, medications, and vitals sign recordings were considered. The outcome of interest was the occurrence of a hypoglycemic event (blood glucose < 72 mg/dL) during a patient's ICU stay. Machine learning models used data prior to the second hour of the ICU stay to predict hypoglycemic outcome. Data from 61,575 patients who underwent 82,479 admissions at 199 hospitals were considered in the study. The best-performing predictive model was the eXtreme gradient boosting model (XGBoost), which achieved an area under the received operating curve (AUROC) of 0.85, a sensitivity of 0.76, and a specificity of 0.76. The machine learning model developed has strong discrimination and calibration for the prediction of hypoglycemia in ICU patients. Prospective trials of these models are required to evaluate their clinical utility in averting hypoglycemia within critically ill patient populations.
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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.