文献类型：期刊文献

英文题名：Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network

作者：Wang, Qi[1]; Qiu, Jiahui[1]; Zhou, Yangming[1]; Ruan, Tong[1]; Gao, Daqi[1]; Gao, Ju[2]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Shanghai Shuguang Hospital, Shanghai, 200021, China

年份：2018

起止页码：1587

外文期刊名：Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

收录：EI(收录号：20191006609925)

语种：英文

外文关键词：Diseases - Natural language processing systems - Computer aided diagnosis - Heart

摘要：Coronary artery disease (CAD) is one of the leading causes of cardiovascular disease deaths. CAD condition progresses rapidly, if not diagnosed and treated at an early stage may eventually lead to an irreversible state of the heart muscle death. Invasive coronary arteriography is the gold standard technique for CAD diagnosis. Coronary arteriography texts describe which part has stenosis and how much stenosis is in details. It is crucial to conduct the severity classification of CAD. In this paper, we employ a recurrent capsule network (RCN) to extract semantic relations between clinical named entities in Chinese coronary arteriography texts, through which we can automatically find out the maximal stenosis for each lumen to inference how severe CAD is according to the improved method of Gensini. Experimental results on the corpus collected from Shanghai Shuguang Hospital show that our proposed method achieves an accuracy of 97.0% in the severity classification of CAD. ? 2018 IEEE.