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EagleEye: A Worldwide Disease-Related Topic Extraction System Using a Deep Learning Based Ranking Algorithm and

Beakcheol Jang1, Myeonghwi Kim2, Inhwan Kim1

  • 1Graduate School of Information, Yonsei Univeristy, Seoul 03722, Korea.

Sensors (Basel, Switzerland)
|July 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced infectious disease surveillance system using deep learning to analyze web data for timely outbreak detection. It offers nation-specific insights and visualizations, improving global health monitoring capabilities.

Keywords:
BiLSTMTF-IDFWBiLSTM-TF-IDFWord2Vecdisease-related topic rankinginternet-sourced data

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Area of Science:

  • Public Health
  • Epidemiology
  • Data Science

Background:

  • Globalization and increased travel accelerate disease spread, overwhelming traditional surveillance systems.
  • Existing disease warning systems struggle with censored data and lack timely internationalization.
  • Social and web data offer untapped potential for accurate, real-time disease activity monitoring.

Purpose of the Study:

  • To develop an automated infectious disease surveillance system leveraging diverse internet-sourced data.
  • To propose a deep learning-based algorithm for effective data filtering and ranking.
  • To provide nation-specific, visualized disease outbreak information and topic analysis.

Main Methods:

  • Extraction of emerging disease information from various internet sources.
  • Implementation of a deep learning model for data filtering and ranking.
  • Development of visualization tools including maps, graphs, charts, and word clouds.

Main Results:

  • The system successfully extracts and analyzes nation-specific disease outbreak data.
  • Disease-related topics are ranked, and report frequencies are quantified per district and disease.
  • Visualizations provide clear insights into disease activities and trends.

Conclusions:

  • The developed system offers a timely and accurate solution for infectious disease surveillance.
  • It addresses limitations of traditional systems by utilizing big data and advanced algorithms.
  • The automated, web-based service is freely accessible, enhancing global public health preparedness.