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Multi-Model Fusion-Based Hierarchical Extraction for Chinese Epidemic Event.

Zenghua Liao1, Zongqiang Yang1, Peixin Huang1

  • 1Laboratory for Big Data and Decision, National University of Defense Technology, Changsha, China.

Data Science and Engineering
|January 9, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for Chinese epidemic event extraction (EE) to improve COVID-19 surveillance. The multi-model fusion-based hierarchical event extraction (MFHEE) architecture effectively identifies epidemic events and arguments from case reports.

Keywords:
COVID-19Event extractionHierarchical extractionMulti-model fusion

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Area of Science:

  • Computational linguistics
  • Epidemiology
  • Public health informatics

Background:

  • Coronavirus disease 2019 (COVID-19) poses a global health challenge, necessitating robust surveillance systems.
  • Extracting structured information from epidemic case reports is crucial for effective outbreak control.

Purpose of the Study:

  • To develop and evaluate a novel method for Chinese epidemic event extraction (EE) from COVID-19 case reports.
  • To improve the accuracy and efficiency of identifying epidemic-related events and their arguments.

Main Methods:

  • Definition of epidemic-related event types and argument roles.
  • Manual annotation of a Chinese COVID-19 Case Report (CCR) dataset.
  • Proposal of a multi-model fusion-based hierarchical event extraction (MFHEE) architecture.

Main Results:

  • The proposed MFHEE architecture demonstrates superior performance in extracting epidemic events on the CCR dataset compared to baseline methods.
  • Experimental results on generic datasets indicate good scalability and portability of the MFHEE model.
  • Ablation studies confirm the significant contributions of the hierarchical structure and multi-model fusion strategy to model precision.

Conclusions:

  • The MFHEE model offers an effective solution for Chinese epidemic event extraction, enhancing COVID-19 surveillance capabilities.
  • The multi-model fusion strategy addresses recognition bias, leading to more accurate event extraction.
  • The developed dataset and model provide valuable resources for advancing research in epidemic intelligence.