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

Hui GeKeyan GaoShaoqiong LiWei WangQiang ChenXialv LinZiyi HuanXuemei SuXu Yang
Published in: International journal of environmental research and public health (2021)
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.
Keyphrases
  • deep learning
  • machine learning
  • public health
  • single cell
  • healthcare
  • neural network
  • mental health