Modeling and Prediction of Invasive Systolic Blood Pressure after General Anesthesia Based on Fusion Algorithm

Abstract

During surgery, invasive systolic blood pressure is an important basis for doctors to judge the patient's life state, which will directly affect the security of the surgery. Accurately predict the changes of invasive systolic blood pressure during general anesthesia help to reduce the risk of surgery. In order to cope with the increasing surgical risk by fluctuations of invasive systolic blood pressure, this paper optimized and combined the traditional machine learning algorithm, and put forward a new fusion algorithm to predict the invasive systolic blood pressure after general anesthesia. In the modeling process, the patients’ basic physical conditions, disease status, and intraoperative data collected by monitoring instrument during the surgical preparation stage were used as characteristic variable. In this paper, Linear Regression, Support Vector Machine Regression, Lasso Regression, and Ridge Regression were used to establish the new fusion algorithm. When the absolute error within 15mmHg, the fusion algorithm's predicting accuracy of invasive systolic blood pressure after general anesthesia reached 91.5%. The accurate prediction of invasive systolic blood pressure after general anesthesia in the preparation stage provides sufficient time for doctors to respond and reduces the risk of surgery to a certain extent.