Development and evaluation of a machine learning-based model for predicting deep vein thrombosis risk in lower limbs following hemorrhagic stroke
Objective To develop a predictive model utilizing machine learning algorithms to forecast the risk of lower extremity deep vein thrombosis(DVT)following hemorrhagic stroke,thereby providing a reference for clinical management decisions.Methods A total of 351 hospitalized patients with acute hemorrhagic stroke pa-tients hospitalized at the Affiliated Hospital of Zunyi Medical University between January 2019 and December 2023 were selected as the study population.The dataset was divided into DVT group(n=155)and non-DVT group(n=196).The prognostic features were collected via the hospital's electronic health record system.Fea-ture reduction was performed using the Least Absolute Shrinkage and Selection Operator(LASSO)regression.Six different machine learning algorithms were deployed to construct the models,including Linear Regression(LR),Support Vector Classification(SVC),Random Forest Classifier(RFC),Elastic Net(EN),Gradient Boosting Classifier(GBC),and Multilayer Perceptron MLPClassifier(MLPC).Model feature importance was numerically expressed using SHapley Additive exPlanations(SHAP).Results LASSO regression identified 10 predictive factors in the training set for model construction,including bed rest time ≥72 hours,acute infection(within one month),hematocrit,left lower limb muscle strength,blood transfusion,hospital stay,D-dimer,age,serum creatinine,and right lower limb muscle strength.The model constructed by the random forest algo-rithm had an area under the curve(AUC)of 0.95,and the top five features ranked by SHAP were bed rest time≥72 hours,acute infection(within one month),D-dimer,age,and serum creatinine.Conclusion The estab-lishment of a risk warning model for lower extremity deep vein thrombosis in hemorrhagic stroke based on ma-chine learning can effectively identify the risk of DVT occurrence,providing a reference for clinical prevention and intervention.
hemorrhagic strokedeep venous thrombosismachine learningearly warning model
万方数据
维普
