BMC endocrine disorders · 2026
Electronic health record-derived machine learning model for hypoglycemia risk prediction in type 2 diabetes mellitus patients: development and validation.
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Ran Qian, Qi Xia, Liu Li, Luo Yunqiu, Cheng Hong, Xu Weiwei, Zhao Xili
Аннотация
BACKGROUND: Hypoglycemia is a serious complication of diabetes. Early recognition of hypoglycemia can improve clinical prognosis, however, traditional diagnostic tools are often limited. Machine learning offers a promising approach for predicting adverse outcomes in diabetic patients.
OBJECTIVE: This study aims to develop and validate machine learning-based models to predict the risk of hypoglycemia in type 2 diabetes mellitus (T2DM) patients.
METHODS: A cohort study design was employed. Clinical data were collected from the electronic health record system. The dataset was randomly partitioned into training and validation subsets using a 7:3 ratio. Four machine learning algorithms, logistic regression (LR), Extreme Gradient Boosting (XGBoost), random forest (RF), and support vector machine (SVM) were implemented to develop hypoglycemia risk prediction models. Predictive performance was assessed using sensitivity, specificity, accuracy, precision, F1 score, and the area under the receiver operating characteristic curve (AUC).
RESULTS: 831 T2DM patients were included, the hypoglycemia incidence was 22.0%. In the training cohort, the AUC for the LR, XGBoost, SVM, and RF models were 0.82, 0.86, 0.84, and 0.80, and corresponding AUCs were 0.76, 0.78, 0.72, and 0.75 in the validation cohort. The XGBoost demonstrated the highest overall predictive performance. Feature importance analysis based on the XGBoost model identified creatinine, triglycerides, albumin, HbA1c, C-peptide, aspartate aminotransferase, hemoglobin, and sulfonylurea use as the most influential predictors of hypoglycemia risk.
CONCLUSIONS: The XGBoost model exhibited superior predictive performance for achieving the higher AUC, F1 score, greater accuracy, sensitivity and specificity. This model enables effective identification of T2DM patients who may require intensified monitoring or targeted interventions to prevent hypoglycemic events.
CLINICAL TRIAL NUMBER: Not applicable.
OBJECTIVE: This study aims to develop and validate machine learning-based models to predict the risk of hypoglycemia in type 2 diabetes mellitus (T2DM) patients.
METHODS: A cohort study design was employed. Clinical data were collected from the electronic health record system. The dataset was randomly partitioned into training and validation subsets using a 7:3 ratio. Four machine learning algorithms, logistic regression (LR), Extreme Gradient Boosting (XGBoost), random forest (RF), and support vector machine (SVM) were implemented to develop hypoglycemia risk prediction models. Predictive performance was assessed using sensitivity, specificity, accuracy, precision, F1 score, and the area under the receiver operating characteristic curve (AUC).
RESULTS: 831 T2DM patients were included, the hypoglycemia incidence was 22.0%. In the training cohort, the AUC for the LR, XGBoost, SVM, and RF models were 0.82, 0.86, 0.84, and 0.80, and corresponding AUCs were 0.76, 0.78, 0.72, and 0.75 in the validation cohort. The XGBoost demonstrated the highest overall predictive performance. Feature importance analysis based on the XGBoost model identified creatinine, triglycerides, albumin, HbA1c, C-peptide, aspartate aminotransferase, hemoglobin, and sulfonylurea use as the most influential predictors of hypoglycemia risk.
CONCLUSIONS: The XGBoost model exhibited superior predictive performance for achieving the higher AUC, F1 score, greater accuracy, sensitivity and specificity. This model enables effective identification of T2DM patients who may require intensified monitoring or targeted interventions to prevent hypoglycemic events.
CLINICAL TRIAL NUMBER: Not applicable.
Предметные рубрики MeSH
HumansDiabetes Mellitus, Type 2HypoglycemiaElectronic Health RecordsFemalePredictive Learning ModelsMaleMachine LearningMiddle AgedPrognosisBoosting Machine Learning AlgorithmsRandom ForestClassification AlgorithmsPrediction AlgorithmsAgedRisk AssessmentRisk FactorsFollow-Up StudiesHypoglycemic AgentsROC CurveCohort Studies