基于重症监护医学信息市场-Ⅳ数据库的老年2型糖尿病脑梗死死亡风险可解释预测OA

Objective To develop an interpretable machine learning model for predicting mortality risk in elderly intensive care unit(ICU)patients with type 2 diabetes mellitus(T2DM)and cerebral infarction,and to identify critical prognostic factors.Methods We extracted data of 514 elderly patients with T2DM and cere-bral infarction from the Medical Information Mart for Intensive Care-Ⅳ database.The dataset was partitioned into training and test sets(7∶3 ratio)via scikit-learn.Within the training set,collinearity analysis was conducted,and features with variance inflation factor>5 were excluded.Lasso regression was further adopted to refine the feature selection.Six machine learning models—eXtreme Gradient Boosting(XGBoost),Logistic regression,LightGBM,AdaBoost,decision tree,and gradient boosting decision tree—were constructed and subjected to rigorous five-fold cross-validation.The optimal model was interpreted by SHAP analysis on the test set to deter-mine the hierarchy of mortality-associated predictors and their nonlinear interactions.Results The XGBoost mod-el demonstrated the best training performance and prediction generalization ability.The area under the curve for 30-day and 365-day mortality risk were 0.928(95%CI=0.853-0.995)and 0.882(95%CI=0.800-0.963),respectively.SHAP analysis revealed that the Oxford Acute Severity of Illness Score,length of hospital stay,con-gestive heart failure,length of ICU stay,peripheral capillary oxygen saturation,and heart rate were the top six predictive factors for 30-day mortality risk,while blood urea nitrogen,Oxford Acute Severity of Illness Score,peripheral capillary oxygen saturation,age,heart rate,and respiratory rate were the top six predictive factors for 365-day mortality risk.Conclusion The XGBoost model shows significant potential in predicting mortality risk in elderly ICU patients with T2DM and cerebral infarction,underscoring the importance of key clinical predictors.