Constructing a knowledge-based heterogeneous information graph for medical health status classification

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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Notes

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    https://www.cdc.gov/nchs/nhanes/index.htm.

  2. 2.

    https://doi.org/10.3886/ICPSR31482.v3.

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Acknowledgements

The work is conducted with approval from the Human Research Ethics Committee of the University of Southern Queensland, Australia (Approval ID: H18REA049). The authors acknowledge the use of the National Health and Nutrition Examination Survey (NHANES) and National Ambulatory Medical Care Survey (NAMCS) in the study and especially, thank the Centers for Disease Control and Prevention of the Department of Health and Human Services, the United States for making the data set publicly available for research purpose. The authors also appreciate the courtesy of the U.S. National Library of Medicine for allowing the use of MEDLINE.

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Correspondence to Thuan Pham.

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Pham, T., Tao, X., Zhang, J. et al. Constructing a knowledge-based heterogeneous information graph for medical health status classification. Health Inf Sci Syst 8, 10 (2020). https://doi.org/10.1007/s13755-020-0100-6

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Keywords

  • Knowledge graph
  • Electronic health data
  • Classification
  • Healthcare