Machine Learning-Based Novel Sedative Drug Clinical Trial Efficacy Analysis and Prediction System

In order to explore the patient's intraoperative and postoperative adverse reactions, patient's postoperative vital signs and patient's satisfaction and their related problems in the clinical trials of the new sedative drugs, this paper pre-processes the initial data, established a random forest model, used the student t-test to analyze the differences, and uses the logistic regression model to make an in-depth analysis. The LSTM time series prediction model is used to predict the IPI data within 3 minutes after drug administration based on the patient's medication information and basic information. Finally, the reason for using correlation analysis in the analysis of factors related to postoperative satisfaction is that the correlation coefficient analysis is first performed to identify the factors related to postoperative satisfaction. Subsequently, multiple linear regression models are used to determine the relationship between these factors and postoperative satisfaction.