Accurate prediction of hypoglycemia and hyperglycemia using machine learning in critically ill patients.

ABSTRACT: Hypoglycemia and hyperglycemia are common complications among critically ill patients. Maintaining blood glucose levels in the normal range is crucial but challenging due to complex influencing factors. This study aimed to develop a machine learning model that predicts hypo- or hyperglycemia 6 h in advance in patients admitted to the intensive care unit (ICU). We analyzed electronic health records of 8,853 ICU patients (1,350,097 records) from a single center in Japan (2010-2022). Hypoglycemia and hyperglycemia were defined as blood glucose levels ≤ 80 mg/dL (4.4 mmol/L) and ≥ 180 mg/dL (10 mmol/L), respectively. We developed prediction models using routinely collected ICU data, including demographic, physiological, laboratory, and treatment variables. Machine learning models were develope

SUBMITTER: Ikeda Y 

PROVIDER: S-EPMC12770608 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature