A System for Recognizing Entities and Extracting Relations from Electronic Medical Records

Abstract

Digging rich knowledge from clinical texts becomes a popular topic today. Knowledge graph has been widely used to integrate and manage abundant knowledge. Entity recognition and relation extraction play important roles in constructing knowledge graphs. In this paper, we develop a system to recognize entities and extract their relations from clinical texts in Electronic Medical Records. Our system implements four major functions: manual entity annotation, automatic entity recognition, manual relation annotation and automatic relation extraction. Tools of entity annotation and relation annotation are designed for professionals to help them manually annotate objects given original clinical texts. Moreover, entity recognition and relation recognition, which CRF and CNN are applied in, are accessible for professionals before manual annotation in order to increase the efficiency. Our system has been used in several applications, such as medical knowledge graph construction and health QA system.