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Mining health knowledge graph for health risk prediction

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Abstract

Nowadays classification models have been widely adopted in healthcare, aiming at supporting practitioners for disease diagnosis and human error reduction. The challenge is utilising effective methods to mine real-world data in the medical domain, as many different models have been proposed with varying results. A large number of researchers focus on the diversity problem of real-time data sets in classification models. Some previous works developed methods comprising of homogeneous graphs for knowledge representation and then knowledge discovery. However, such approaches are weak in discovering different relationships among elements. In this paper, we propose an innovative classification model for knowledge discovery from patients’ personal health repositories. The model discovers medical domain knowledge from the massive data in the National Health and Nutrition Examination Survey (NHANES). The knowledge is conceptualised in a heterogeneous knowledge graph. On the basis of the model, an innovative method is developed to help uncover potential diseases suffered by people and, furthermore, to classify patients’ health risk. The proposed model is evaluated by comparison to a baseline model also built on the NHANES data set in an empirical experiment. The performance of proposed model is promising. The paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. In addition, by accessing the patterns of various observations, the research contributes to the work of practitioners by providing a multifaceted understanding of individual and public health.

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Notes

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

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Acknowledgements

The work presented in this manuscript is a significant extensive work of its preliminary form, which is published with a title “Mining Heterogeneous Information Graph for Health Status Classification” in the Proceedings of the 6th International Conference on Behavioral, Economic, and Socio-Cultural Computing, Kaohsiung, Taiwan 12–14 November 2018 (DOI: 10.1109/BESC.2018.8697292). The work is conducted with consent from the Human Research Ethics Committee of the University of Southern Queensland, Australia (Approval ID: H18REA049).

The work is partially supported by the National Natural Science Foundation of China (NSFC 71801217). The authors also like to acknowledge the use of National Health and Nutrition Examination Survey (NHAMES) in the study and specifically, thank Centers for Disease Control and Prevention of the Department of Health and Human Services, United States for making the data set publicly available for research purpose.

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Authors and Affiliations

  1. University of Southern Queensland, Toowoomba, Australia

    Xiaohui Tao, Thuan Pham, Ji Zhang, Jianming Yong & Wee Pheng Goh

  2. Renmin University of China, Beijing, China

    Wenping Zhang

  3. South China University of Technology, Guangdong Province, China

    Yi Cai

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  1. Xiaohui Tao
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Correspondence to Xiaohui Tao.

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Tao, X., Pham, T., Zhang, J. et al. Mining health knowledge graph for health risk prediction. World Wide Web 23, 2341–2362 (2020). https://doi.org/10.1007/s11280-020-00810-1

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