Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network

TL;DR

This paper introduces a recurrent capsule network approach to automatically classify the severity of coronary artery disease from Chinese arteriography texts, achieving high accuracy and aiding early diagnosis.

Contribution

The study presents a novel recurrent capsule network model for extracting semantic relations in medical texts to classify CAD severity automatically.

Findings

Achieved 97.0% accuracy in CAD severity classification

Effectively extracts semantic relations from Chinese medical texts

Improves early diagnosis of coronary artery disease

Abstract

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.