Constructing a knowledge-based heterogeneous information graph for medical health status classification
Article
Article Title | Constructing a knowledge-based heterogeneous information graph for medical health status classification |
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ERA Journal ID | 212669 |
Article Category | Article |
Authors | Pham, Thuan (Author), Tao, Xiaohui (Author), Zhang, Ji (Author) and Yong, Jianming (Author) |
Journal Title | Health Information Science and Systems |
Journal Citation | 8 (1) |
Article Number | 10 |
Number of Pages | 14 |
Year | 2020 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 2047-2501 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s13755-020-0100-6 |
Web Address (URL) | https://link.springer.com/article/10.1007/s13755-020-0100-6 |
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. |
Keywords | knowledge graph, electronic health data, classification, healthcare |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460902. Decision support and group support systems |
469999. Other information and computing sciences not elsewhere classified | |
460208. Natural language processing | |
420308. Health informatics and information systems | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Sciences |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q59zw/constructing-a-knowledge-based-heterogeneous-information-graph-for-medical-health-status-classification
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