Clinical efficacy of new sedative drugs based on machine learning big data statistics Analysis and prediction

Abstract

In order to solve the analysis and prediction of the clinical efficacy of sedative drugs and the problems of adverse drug reactions, various methods of differential analysis, correlation analysis, regression prediction and data sampling are used to explore the connection between the clinical efficacy analysis and prediction and adverse reactions. The new drug group R and traditional drug group B were subdivided into intraoperative adverse reactions (cough, body movement and other intraoperative reactions) and 24 hours postoperative adverse reactions (dizziness, headache, drowsiness, fatigue, abdominal distension and abdominal pain, and other postoperative reactions). After Spearman medical statistical correlation analysis, the significance scale and correlation coefficient thermal map were obtained, and the important factors with P value <0.05 were selected. A normality analysis of the underlying signs of patients under different agents then confirmed that subject selection was without sample selection bias. Considering the paired relationship between the two sedative groups and the three corresponding vital signs (e. g., heart rate, pulse, blood pressure), the paired T test was used to assess significant differences. Finally, a multiple linear regression model was used to analyze the cause of the difference and predict the IPI within 3 minutes of drug administration based on medication information and patient information. R drugs had a positive effect on patient satisfaction, while the total dose of sedatives, the total dose of analgesics, anesthesiologist satisfaction, and endoscopist satisfaction had a negative impact on patient satisfaction. These three vital signs differ between the new drug and traditional drug groups. Based on the development of artificial intelligence in the new era, taking new drugs as the background and combining with medical clinical experiments, the application prospect of clinical experiments and points out the current shortcomings, aiming to provide basis for clinical experiments of new sedative drugs in the future.