Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network
This work addresses the need for early and accurate CAD diagnosis to prevent heart muscle death, but it is incremental as it applies a hybrid method to a specific medical domain.
The paper tackled the problem of automatically classifying the severity of coronary artery disease (CAD) by analyzing Chinese coronary arteriography texts, achieving an accuracy of 97.0% using a recurrent capsule network.
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.