Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients
作者全名："Xu, Qianjie; Lei, Haike; Li, Xiaosheng; Li, Fang; Shi, Hao; Wang, Guixue; Sun, Anlong; Wang, Ying; Peng, Bin"
作者地址："[Xu, Qianjie; Peng, Bin] Chongqing Med Univ, Sch Publ Hlth, Dept Hlth Stat, Chongqing 400016, Peoples R China; [Lei, Haike; Li, Xiaosheng; Li, Fang; Shi, Hao; Sun, Anlong; Wang, Ying] Chongqing Univ, Chongqing Canc Multiom Big Data Applicat Engn Res, Canc Hosp, Chongqing 400030, Peoples R China; [Wang, Guixue] Coll Bioengn Chongqing Univ, State & Local Joint Engn Lab Vasc Implants, MOE Key Lab Biorheol Sci & Technol, Chongqing 400030, Peoples R China"
通信作者："Peng, B (通讯作者)，Chongqing Med Univ, Sch Publ Hlth, Dept Hlth Stat, Chongqing 400016, Peoples R China.; Sun, AL; Wang, Y (通讯作者)，Chongqing Univ, Chongqing Canc Multiom Big Data Applicat Engn Res, Canc Hosp, Chongqing 400030, Peoples R China."
关键词：Gastric cancer; Venous thromboembolism; Prediction model; Machine learning
摘要："Stomach cancer (GC) has one of the highest rates of thrombosis among cancers and can lead to considerable morbidity, mortality, and additional costs. However, to date, there is no suitable venous thromboembolism (VTE) prediction model for gastric cancer patients to predict risk. Therefore, there is an urgent need to establish a clinical prediction model for VTE in gastric cancer patients. We collected data on 3092 patients between January 1, 2018 and December 31, 2021. And after feature selection, 11 variables are reserved as predictors to build the model. Five machine learning (ML) algorithms are used to build different VTE predictive models. The accuracy, sensitivity, specificity, and AUC of these five models were compared with traditional logistic regression (LR) to recommend the best VTE prediction model. RF and XGB models have selected the essential characters in the model: Clinical stage, Blood Transfusion History, D-Dimer, AGE, and FDP. The model has an AUC of 0.825, an accuracy of 0.799, a sensitivity of 0.710, and a specificity of 0.802 in the validation set. The model has good performance and high application value in clinical practice, and can identify high-risk groups of gastric cancer patients and prevent venous thromboembolism."
基金机构：Chongqing Performance Incentive and Guidance Project for Scientific Research In-stitutions [cstc2020jxjl130016]
基金资助正文：Funding Support for this work was provided by the Chongqing Performance Incentive and Guidance Project for Scientific Research In-stitutions (cstc2020jxjl130016) .