Judgment and Prevention of Urinary Tract Injury in Gynecological Surgery Based on Data Mining

Abstract

In this paper, a data mining-enabled model is developed to analyze the case-related data of 39 patients with urinary tract injury who underwent laparoscopic surgery in a certain hospital from 2012 to 2017. Statistics on the history and characteristics of the case data summarized and analyzed the causes of urinary tract injury and the urinary system. The relationship between the occurrence of injury and the type of surgery and the treatment and preventive measures taken for urinary tract injury during and after surgery are summarized. The statistical method with SPSS16.0 statistical software was used to analyze the data of this study, and the X2 test was used to compare the rates. The differences of $P\le 0.05$ and $P\le 0.01$ were statistically significant. Laparoscopic surgery in gynecology is a minimally invasive technique, but it is still accompanied by the possibility of complications. During the experimental setup and implementation, we have observed that among 8742 cases of laparoscopic surgery complicated by urinary tract injury, there were 39 cases with a rate of 0.45%. In the past five years, the incidence of urinary tract injury in gynecological surgery in our country has increased year by year, and the number of cases of urinary tract injury has also increased year by year. Through analysis, it is found that the cause of the injury is related to the level of surgery, pelvic adhesion, and energy equipment. Based on the above problems, according to the clinical data of patients with urinary tract injury complicated by gynecological surgery in the hospital, the relevant factors of gynecological surgery complicated by urinary tract injury are analyzed to improve the awareness of urinary tract protection and prevention of injury during the operation and preventive measures are actively taken to avoid medical treatment.