Mining heterogeneous information graph for health status classification

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 Zhang, Wenping and Cai, Yi (2018) Mining heterogeneous information graph for health status classification. In: 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2018), 12-14 Nov 2018, Kaohsiung, Taiwan.

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Abstract

In the medical domain, there exists a large volume of data from multiple sources such as electronic health records, general health examination results, and surveys. The data contain useful information reflecting people’s health and provides great opportunities for studies to improve the quality of healthcare. However, how to mine these data effectively and efficiently still remains a critical challenge. In this paper, we propose an innovative classification model for knowledge discovery from patients’ personal health repositories. By based on analytics of massive data in the National Health and Nutrition Examination Survey, the study builds a classification model to classify patients’health status and reveal the specific disease potentially suffered
by the patient. This paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. Moreover, this research contributes to the healthcare community by providing a deep understanding of people’s health with
accessibility to the patterns in various observations.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Accepted version deposited in accordance with the copyright policy of the publisher. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Faculty/School / Institute/Centre: Historic - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 Jul 2013 - 17 Jan 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 Jul 2013 - 17 Jan 2021)
Date Deposited: 29 Apr 2020 06:21
Last Modified: 24 Jul 2020 02:45
Uncontrolled Keywords: heterogeneous information graph, classification, healthcare
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing
08 Information and Computing Sciences > 0803 Computer Software > 080309 Software Engineering
Identification Number or DOI: https://doi.org/10.1109/BESC.2018.8697292
URI: http://eprints.usq.edu.au/id/eprint/35620

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