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An Automatic Approach Designed for Inference of the Underlying Cause-of-Death of Citizens

Author

Listed:
  • Hui Ge

    (Chinese Center for Disease Control and Prevention, Beijing 102206, China)

  • Keyan Gao

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China)

  • Shaoqiong Li

    (Chinese Center for Disease Control and Prevention, Beijing 102206, China)

  • Wei Wang

    (Chinese Center for Disease Control and Prevention, Beijing 102206, China)

  • Qiang Chen

    (Chinese Center for Disease Control and Prevention, Beijing 102206, China)

  • Xialv Lin

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China)

  • Ziyi Huan

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China)

  • Xuemei Su

    (Chinese Center for Disease Control and Prevention, Beijing 102206, China)

  • Xu Yang

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China)

Abstract

It is very important to have a comprehensive understanding of the health status of a country’s population, which helps to develop corresponding public health policies. Correct inference of the underlying cause-of-death for citizens is essential to achieve a comprehensive understanding of the health status of a country’s population. Traditionally, this relies mainly on manual methods based on medical staff’s experiences, which require a lot of resources and is not very efficient. In this work, we present our efforts to construct an automatic method to perform inferences of the underlying causes-of-death for citizens. A sink algorithm is introduced, which could perform automatic inference of the underlying cause-of-death for citizens. The results show that our sink algorithm could generate a reasonable output and outperforms other stat-of-the-art algorithms. We believe it would be very useful to greatly enhance the efficiency of correct inferences of the underlying causes-of-death for citizens.

Suggested Citation

  • Hui Ge & Keyan Gao & Shaoqiong Li & Wei Wang & Qiang Chen & Xialv Lin & Ziyi Huan & Xuemei Su & Xu Yang, 2021. "An Automatic Approach Designed for Inference of the Underlying Cause-of-Death of Citizens," IJERPH, MDPI, vol. 18(5), pages 1-11, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:5:p:2414-:d:508640
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    References listed on IDEAS

    as
    1. Boumezoued, Alexandre & Hardy, Héloïse Labit & El Karoui, Nicole & Arnold, Séverine, 2018. "Cause-of-death mortality: What can be learned from population dynamics?," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 301-315.
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