首页|Affiliated Hospital of Fujian Medical University Reports Findings in Venous Thro mboembolism (Machine learning models for prediction of postoperative venous thro mboembolism in gynecological malignant tumor patients)
Affiliated Hospital of Fujian Medical University Reports Findings in Venous Thro mboembolism (Machine learning models for prediction of postoperative venous thro mboembolism in gynecological malignant tumor patients)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Cardiovascular Disease s and Conditions - Venous Thromboembolism is the subject of a report. According to news reporting from Fuzhou, People’s Republic of China, by NewsRx journalists , research stated, “To identify risk factors that associated with the occurrence of venous thromboembolism (VTE) within 30 days after hysterectomy among gynecol ogical malignant tumor patients, and to explore the value of machine learning (M L) models in VTE occurrence prediction. A total of 1087 patients between January 2019 and January 2022 with gynecological malignant tumors were included in this single-center retrospective study and were randomly divided into the training d ataset (n = 870) and the test dataset (n = 217).” The news correspondents obtained a quote from the research from the Affiliated H ospital of Fujian Medical University, “Univariate logistic regression analysis w as used to identify risk factors that associated with the occurrence of postoper ative VTE in the training dataset. Machine learning models (including decision t ree (DT) model and logistic regression (LR) model) to predict the occurrence of postoperative VTE were constructed and internally validated. The incidence of de veloping 30-day postoperative VTE was 6.0% (65/1087). Age, previou s VTE, length of stay (LOS), tumor stage, operative time, surgical approach, lym phadenectomy (LND), intraoperative blood transfusion and gynecologic Caprini (G- Caprini) score were identified as risk factors for developing postoperative VTE in gynecological malignant tumor patients (p <0.05). The A UCs of LR model and DT model for predicting VTE were 0.722 and 0.950, respective ly.”
Fuzhou, People’s Republic of China, Asia , Cardiovascular Diseases and Conditions, Cyborgs, Embolism and Thrombosis, Emer ging Technologies, Gynecology, Health and Medicine, Hematology, Machine Learning , Risk and Prevention, Thromboembolism, Vascular Diseases and Conditions, Venous Thromboembolism
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