Relation Extraction for Chinese Clinical Records Using Multi-View Graph Learning

• Published in: IEEE access Vol. 8; pp. 215613 - 215622
• Main Authors: Ruan, Chunyang, Wu, Yingpei, Sheng Luo, Guang, Yang, Yun, Ma, Pingchuan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Chinese clinical records; Data mining; Diseases; Domains; graph attention networks; Graph representations; Graphical representations; Herbs; Knowledge engineering; Learning; Learning systems; Medical records; Relation extraction; representation learning; Traditional Chinese medicine
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3037086

Relation extraction is a necessary step in obtaining information from clinical medical records. In the medical domain, there have been several studies on relation extraction in modern medicine clinical notes written in English. However, very limited relation extraction research has been conducted on clinical notes written in Chinese, especially traditional Chinese medicine (TCM) clinical records (e.g., herb-symptom, herb-disease). Instead of independently extracting each relation from a single sentence or text, we propose to globally and reasonably extract multiple types of relations from the Chines clinical records with a novel heterogeneous graph representation learning method. Specifically, we first construct multiple view medical entity graphs based on the co-occurring relations, knowledge obtained from the clinic, and domain texts with the corresponding information of two medical entities from the Chinese clinical records, in which each edge is a candidate relation; we then build a Graph Convolutional Network (GCN)-based representation learning with the attention mechanism to simultaneously infer the existence of all the edges via classification. The experimental data were obtained from the Chinese medical records and literature provided by previous work. The main experimental results on Chinese clinical records show that our proposed model's precision, recall, and F1-score reach 10.2%, 13.5%, 12.6%, demonstrating significant improvements over state-of-the-art.