A Risk Prediction Model Establishment for Postoperative Infection in Neurosurgery Based on Data Mining Technology
Objective To establish a risk prediction model for postoperative infection in neurosurgical patients,a methodology involved deep mining and analysis of medical data,and accurately and effectively predict postoperative infection as early as possible.Methods The postoperative data of patients were gathered from the neurosurgery department center of a general hospital in Beijing,comprising 21,239 samples.The mutual information method was applied to screen characteristic variables,and the class imbalance problem was addressed using SMOTE.Subsequently,a random forest model with the best performance was utilized to train a risk prediction model for postoperative infection in neurosurgical patients.Results The accuracy of the prediction model was 0.941,the sensitivity was 0.940,the specificity was 0.941,and the AUC was 0.985(with optimized parameters).It was concluded that the amount of blood loss,diagnostic code,discharge ward,operation name,postoperative blood glucose,and absolute value of postoperative white blood cells were important characteristics of postoperative infection in neurosurgical patients.Conclusion The risk prediction model for postoperative infection in neurosurgical patients proposed in this research can aid in clinical decision-making and early intervention,ultimately contributing to the prevention of postoperative infection.
neurosurgical patientspostoperative infectionrisk prediction model
NETL
NSTL
万方数据
