An investigation into the impact of temporality on COVID-19 infection and mortality predictions: new perspective based on Shapley Values.

Journal: BMC medical research methodology
PMID: 40275181

Abstract

INTRODUCTION: Machine learning models have been employed to predict COVID-19 infections and mortality, but many models were built on training and testing sets from different periods. The purpose of this study is to investigate the impact of temporality, i.e., the temporal gap between training and testing sets, on model performances for predicting COVID-19 infections and mortality. Furthermore, this study seeks to understand the causes of the impact of temporality.

Authors

  • Mingming Chen
  • Qihang Qian
    School of Computer Science and Technology, Zhejiang University of Technology, No. 18 Chaowang Road, Hangzhou, Zhejiang, 310014, P.R. China.
  • Xiang Pan
    Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America.
  • Tenglong Li
    Wisdom Lake Academy of Pharmacy, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.

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