  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.
