Relation Extraction Based on Fusion Dependency Parsing from Chinese EMRs

The Electronic Medical Record (EMR) contains a great deal of medical knowledge related to patients, which has been widely used in the construction of medical knowledge graphs. Previous studies mainly focus on the features based on surface semantics of EMRs for relation extraction, such as contextual feature, but the features of sentence structure in Chinese EMRs have been neglected. In this paper, a fusion dependency parsing-based relation extraction method is proposed. Specifically, this paper extends basic features with medical record feature and indicator feature that are applicable to Chinese EMRs. Furthermore, dependency syntactic features are introduced to analyse the dependency structure of sentences. Finally, the F1 value of relation extraction based on extended features is 4.87% higher than that of relation extraction based on basic features. And compared with the former, the F1 value of relation extraction based on fusion dependency parsing is increased by 4.39%. The results of experiments performed on a Chinese EMR data set show that the extended features and dependency parsing all contribute to the relation extraction

International Academic Symposium of Social Science 2022

This conference proceedings gathers work and research presented at the International Academic Symposium of Social Science 2022 (IASSC2022) held on July 3, 2022, in Kota Bharu, Kelantan, Malaysia. The conference was jointly organized by the Faculty of Information Management of Universiti Teknologi MARA Kelantan Branch, Malaysia; University of Malaya, Malaysia; Universitas Pembangunan Nasional Veteran Jakarta, Indonesia; Universitas Ngudi Waluyo, Indonesia; Camarines Sur Polytechnic Colleges, Philippines; and UCSI University, Malaysia. Featuring experienced keynote speakers from Malaysia, Australia, and England, this proceeding provides an opportunity for researchers, postgraduate students, and industry practitioners to gain knowledge and understanding of advanced topics concerning digital transformations in the perspective of the social sciences and information systems, focusing on issues, challenges, impacts, and theoretical foundations. This conference proceedings will assist in shaping the future of the academy and industry by compiling state-of-the-art works and future trends in the digital transformation of the social sciences and the field of information systems. It is also considered an interactive platform that enables academicians, practitioners and students from various institutions and industries to collaborate