Constructing a knowledge-based heterogeneous information graph for medical 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 (2020) Constructing a knowledge-based heterogeneous information graph for medical health status classification. Health Information Science and Systems, 8 (1):10.


Abstract

Applying Pearson correlation and semantic relations in building a heterogeneous information graph (HIG) to develop a classification model has achieved a notable performance in improving the accuracy of predicting the status of health risks. In this study, the approach that was used, integrated knowledge of the medical domain as well as taking advantage of applying Pearson correlation and semantic relations in building a classification model for diagnosis. The research mined knowledge which was extracted from titles and abstracts of MEDLINE to discover how to assess the links between objects relating to medical concepts. A knowledge-base HIG model then was developed for the prediction of a patient’s health status. The results of the experiment showed that the knowledge-base model was superior to the baseline model and has demonstrated that the knowledge-base could help improve the performance of the classification model. The contribution of this study has been to provide a framework for applying a knowledge-base in the classification model which helps these models achieve the best performance of predictions. This study has also contributed a model to medical practice to help practitioners become more confident in making final decisions in diagnosing illness. Moreover, this study affirmed that biomedical literature could assist in building a classification model. This contribution will be advantageous for future researchers in mining the knowledge-base to develop different kinds of classification models.


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Item Type: Article (Commonwealth Reporting Category C)
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 Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Date Deposited: 28 Apr 2020 23:33
Last Modified: 08 May 2020 01:59
Uncontrolled Keywords: knowledge graph, electronic health data, classification, healthcare
Fields of Research (2008): 08 Information and Computing Sciences > 0806 Information Systems > 080605 Decision Support and Group Support Systems
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 > 0807 Library and Information Studies > 080702 Health Informatics
Identification Number or DOI: 10.1007/s13755-020-0100-6
URI: http://eprints.usq.edu.au/id/eprint/38137

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