使用資料探勘來分析與預測麻醉術後噁心嘔吐

Objective: This study was based on the data of a Would-be Academic Medical Center hospital in northern Taiwan. It used data mining technology to construct a characteristic model for predicting the high-risk group of postoperative nausea and vomiting (PONV), and provided a reference for clinical diagnosis and care. Methods: Firstly, using the chi-square test of SPSS 18.0 and logistic regression, the related factors and influencing factors of postoperative nausea and vomiting were identified, and then the R software version 3.5.3 was used to divide the data into training set and verification set for further logistic regression (GLM), decision tree (rpart), support vector machine (SVM), and neural network (neuralnet) module prediction, with accuracy, specificity, sensitivity, PPV, NPV, and F1 value to index the performance of the algorithm. Result: Factors significantly associated with PONV were gender, smoking, alcohol abuse, and postoperative symptoms (including postoperative sore throat, and postoperative dizziness). In the logistic regression analysis, the risk of women having PONV was 2.978 times higher than men. The risk of PONV in patients with sore throat after surgery was 2.305 times higher than those without. The risk of PONV in patients with dizziness after surgery was 2.943 times higher than those without. The risk of PONV in the surgical category of weight loss surgery was 4.528 times that of fracture reduction surgery. The prediction accuracy rate was 64.35% in logistic regression, 71.83% in decision Tree, 75.65% in SVM, and 75.30% in neural network. Conclusion: We identified factors that affect PONV and the pros and cons of different predictive models.