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Australasian Conference on Data Mining

AusDM 2019: Data Mining pp 64–76Cite as

Estimating County Health Indices Using Graph Neural Networks

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1127))

Abstract

Population health analytics is fundamental to developing responsive public health promotion programs. A traditional method to interpret health statistics at population level is analyzing data aggregated from individuals, typically through telephone surveys. Recent studies have found that social media can be utilized as an alternative population health surveillance system, providing quality and timely data at virtually no cost. In this paper, we further investigate the use of social media to the task of population health estimation, based on a graph neural network approach. Specifically, we first introduce a graph modeling method to construct the representation of each county as a graph of interactions between health-related features in the community. We then adopt a graph neural network model to learn the population health representation, ended by a regression layer, to estimate the health indices. We validate our proposed method by large-scale experiments on Twitter data for the task of predicting health indices of the US counties. Empirical results show a significant correlation with the reported health statistics, up to a Spearman correlation coefficient (\(\rho \)) value of 0.69, and that our graph-based approach outperforms the existing methods. These promising results also suggest potential application of graph-based models to a range of societal-level analytics tasks through social media.

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Notes

  1. 1.

    https://www.cdc.gov/chronicdisease/resources/publications/aag/brfss.htm.

  2. 2.

    https://www.cdc.gov/brfss/.

  3. 3.

    https://www.cdc.gov/brfss/index.html.

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Author information

Authors and Affiliations

  1. Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia

    Hung Nguyen & Thin Nguyen

  2. School of Information Technology, Deakin University, Geelong, Australia

    Duc Thanh Nguyen

Authors
  1. Hung Nguyen
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  2. Duc Thanh Nguyen
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  3. Thin Nguyen
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Corresponding author

Correspondence to Hung Nguyen .

Editor information

Editors and Affiliations

  1. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia

    Thuc D. Le

  2. La Trobe University, Melbourne, Australia

    Kok-Leong Ong

  3. CSIRO Scientific Computing, Canberra, Australia

    Yanchang Zhao

  4. CSIRO Scientific Computing, Canberra, Australia

    Warren H. Jin

  5. Consilium Technology, Adelaide, Australia

    Sebastien Wong

  6. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia

    Lin Liu

  7. Microsoft Proprietary Limited, Singapore, Singapore

    Graham Williams

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© 2019 Springer Nature Singapore Pte Ltd.

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Cite this paper

Nguyen, H., Nguyen, D.T., Nguyen, T. (2019). Estimating County Health Indices Using Graph Neural Networks. In: Le, T., et al. Data Mining. AusDM 2019. Communications in Computer and Information Science, vol 1127. Springer, Singapore. https://doi.org/10.1007/978-981-15-1699-3_6

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  • DOI: https://doi.org/10.1007/978-981-15-1699-3_6

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1698-6

  • Online ISBN: 978-981-15-1699-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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