点击次数：

论文类型：会议论文

发表时间：2021-01-08

卷号：2018-January

页面范围：164-169

摘要：Overall survival (OS) prediction has been a central topic of oncology. Most existing OS prediction models are based on traditional statistical methods which becomes a limitation when confronted with high dimensional dataset as well complicated internal relation among features in practice. In this paper, we weaken this limitation by exploring the application of multi-modal hypergraph (MMHG) learning framework to improve the accuracy of prediction. More specifically, the proposed hypergraph model unites the demographics, pathologic characteristics and physiological indicators simultaneously to predict the overall survival after D2 gastrectomy for gastric cancer. Experiments are carried out on a real data set of West China Hospital of Sichuan University with 939 patients to evaluate the proposed approach by comparison with random forest (RF) and support vector machine (SVM). Results demonstrate that our scheme outperforms the baseline methods in overall survival classification performance. © 2017 IEEE.