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Using data mining techniques to fight and control epidemics: A scoping review.

Reza Safdari1, Sorayya Rezayi2, Soheila Saeedi2,3

  • 1Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.

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|May 12, 2021
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Summary
This summary is machine-generated.

This review found Natural Language Processing (NLP) and supervised learning are popular data mining methods for pandemic research, especially for COVID-19. However, more research is needed on disease treatment and control.

Keywords:
COVID-19Data miningPandemicsReview

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Area of Science:

  • Public Health
  • Informatics
  • Epidemiology

Background:

  • Pandemics pose significant public health challenges.
  • Data mining techniques are increasingly used to uncover hidden knowledge in health research.
  • Understanding trends in data mining applications is crucial for advancing pandemic response.

Purpose of the Study:

  • To identify prevalent data mining methods used in pandemic research.
  • To determine common research approaches and knowledge gaps.
  • To analyze the application of data mining in infectious disease and epidemiology.

Main Methods:

  • Systematic literature search of Web of Science, Scopus, and PubMed databases.
  • Scoping review of 50 eligible articles selected using PRISMA guidelines.
  • Analysis and summarization of data mining techniques, applications, and software.

Main Results:

  • Natural Language Processing (NLP) was the most favored data mining method (22%).
  • Revealing disease characteristics was the most common approach (22%), with COVID-19 being the most studied disease.
  • Supervised learning techniques dominated (90%), particularly in infectious disease (36%) and epidemiology.

Conclusions:

  • Data mining, especially NLP and supervised learning, offers valuable insights into diseases during pandemics.
  • Current research predominantly focuses on disease characteristics rather than treatment and control.
  • Further research is needed to bridge the gap in applying data mining for effective disease management and intervention strategies.