MeKG: building a medical knowledge graph by data mining from MEDLINE

Pham, Thuan ORCID: https://orcid.org/0000-0001-7433-858X and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Zhang, Ji and Yong, Jianming and Zhou, Xujuan and Gururajan, Raj (2019) MeKG: building a medical knowledge graph by data mining from MEDLINE. In: 12th International Conference on Brain Informatics (BI 2019), 13-15 Dec 2019, Haikou; China.

Abstract

Mining data on a knowledge level can help to achieve a higher performance of a decision support system. This study built a knowledge graph based on MEDLINE that has a large number of articles in the medical domain. MEDLINE uses Medical Subject Headings (MeSH) for document index. Based on MeSH, articles are extracted from the MEDLINE correspondent to medical subjects. Using the MeSH as the backbone of knowledge base, the MEDLINE articles were used to generate instances which helped to populate the knowledge base. This approach facilitated the creation of a knowledge graph that was capable of discovering the hidden knowledge among concepts of MeSH. The knowledge graph had a significant effect on improving the quality of healthcare. The contribution of the research is on a framework for building knowledge bases. Moreover, the approach provided an essential source at the knowledge level for researchers in healthcare.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 July 2013 -)
Date Deposited: 11 Mar 2020 05:29
Last Modified: 22 Apr 2020 04:42
Uncontrolled Keywords: MeSH, MEDLINE, knowledge graph, data mining
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0806 Information Systems > 080605 Decision Support and Group Support Systems
08 Information and Computing Sciences > 0807 Library and Information Studies > 080702 Health Informatics
Identification Number or DOI: 978-3-030-37078-7_16
URI: http://eprints.usq.edu.au/id/eprint/37948

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