静脉血栓栓塞症患者住院时间机器学习预测模型的建立OA

Objective To compare the performance of five supervised learning algorithms in predicting the length of hospital stay for venous thromboembolism(VTE)patients,so as to develop an effective machine learning prediction model to gain a deeper understanding of the factors that affect the length of hospital stay for VTE patients.Methods This study utilized data from 1 854 VTE patients collected from a tertiary hospital in northwest China from 2018 to 2023,covering six core steps:data preprocessing,missing data processing,attribute encoding,data simplification,model construction,and model testing.Five machine learning algorithms were used for model training and comparison.Results The support vector machine(SVM)model performed the best in predicting hospitalization duration,with an accuracy rate of 94.53%.Through analysis,it was found that hemoglobin,heart disease,age,hypertension,and creatinine were the main factors affecting hospitalization duration.In addition,drug use(such as warfarin sodium)also showed a significant impact on hospitalization duration.Conclusion This study successfully developed an SVM-based prediction model that can accurately predict the length of hospital stay for VTE patients.This model not only provides a tool for hospitals to optimize bed resource allocation and operational efficiency,but also provides better treatment planning and scheduling support for patients.