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

Hui Ge, Keyan Gao, Shaoqiong Li, Wei Wang, Qiang Chen, Xialv Lin, Ziyi Huan, Xuemei Su and Xu Yang
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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

IJERPH, 2021, vol. 18, issue 5, 1-11

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.

Keywords: cause-of-death inference; automatical; public heath; medical service (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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