Using Data Mining Techniques to Fight and Control Epidemics: A Scoping Review

Overview

Journal Health Technol (Berl)

Publisher Springer

Specialty Biomedical Engineering

Date 2021 May 12

PMID 33977022

Citations 3

Authors

Reza Safdari

Sorayya Rezayi

Soheila Saeedi

Mozhgan Tanhapour

Marsa Gholamzadeh

Affiliations

Soon will be listed here.

Abstract

The main objective of this survey is to study the published articles to determine the most favorite data mining methods and gap of knowledge Since the threat of pandemics has raised concerns for public health, data mining techniques were applied by researchers to reveal the hidden knowledge. Web of Science, Scopus, and PubMed databases were selected for systematic searches. Then, all of the retrieved articles were screened in the stepwise process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist to select appropriate articles. All of the results were analyzed and summarized based on some classifications. Out of 335 citations were retrieved, 50 articles were determined as eligible articles through a scoping review. The review results showed that the most favorite DM belonged to Natural language processing (22%) and the most commonly proposed approach was revealing disease characteristics (22%). Regarding diseases, the most addressed disease was COVID-19. The studies show a predominance of applying supervised learning techniques (90%). Concerning healthcare scopes, we found that infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline. The most common software used in the studies was SPSS (22%) and R (20%). The results revealed that some valuable researches conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. But most researches will need in terms of treatment and disease control.

Citing Articles

Text and data mining in health research: reflections on copyright.

Souza A, Schirru L, Alvarenga M Cad Saude Publica. 2024; 40(5):e00169023.

PMID: 38775612 PMC: 11111164. DOI: 10.1590/0102-311XPT169023.

Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead.

Adadi A, Lahmer M, Nasiri S J King Saud Univ Comput Inf Sci. 2023; 34(8):5898-5920.

PMID: 37520766 PMC: 8831917. DOI: 10.1016/j.jksuci.2021.07.010.

Text mining in mosquito-borne disease: A systematic review.

Ong S, Pauzi M, Gan K Acta Trop. 2022; 231:106447.

PMID: 35430265 PMC: 9663275. DOI: 10.1016/j.actatropica.2022.106447.

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