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Text mining in mosquito-borne disease: A systematic review
Song-Quan Ong1, Maisarah Binti Mohamed Pauzi2, Keng Hoon Gan2
1Institute for Tropical Biology and Conservation, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu, Sabah 88400, Malaysia.
Acta Tropica
|April 17, 2022
Summary
Text mining advances offer new ways to combat mosquito-borne diseases like Zika and Dengue. This review surveys techniques, applications, and challenges, highlighting Twitter as a key data source for disease surveillance and treatment insights.
Area of Science:
- * Computational Biology and Bioinformatics
- * Public Health and Epidemiology
- * Natural Language Processing
Background:
- * Mosquito-borne diseases are a growing global health concern, exacerbated by factors like the COVID-19 pandemic.
- * Text mining techniques offer powerful tools for extracting knowledge and insights from vast amounts of unstructured text data related to infectious diseases.
- * A comprehensive review of text mining applications specifically for mosquito-borne diseases was lacking.
Purpose of the Study:
- * To conduct a systematic review and bibliometric analysis of text mining techniques applied to mosquito-borne diseases.
- * To identify dominant diseases, data sources, methodologies, and applications within this research domain.
- * To highlight challenges and propose future research directions for text mining in combating mosquito-borne diseases.
Main Methods:
- * Bibliometric analysis of 294 articles from Scopus and PubMed (2016-2021) on text mining and mosquito-borne diseases.
- * Filtering and review of 158 selected articles to identify 27 relevant studies employing text mining.
- * Categorization of studies based on disease focus, corpus sources, text mining techniques, and applications.
Main Results:
- * The reviewed studies predominantly focused on Zika (38.70%), Dengue (32.26%), and Malaria (29.03%), with limited coverage of other diseases.
- * Twitter emerged as the most frequent data source, followed by PubMed and LexisNexis.
- * Sentiment analysis and information extraction were the most utilized text mining techniques, primarily for disease surveillance and treatment discovery.
Conclusions:
- * Text mining holds significant potential for enhancing the management and surveillance of mosquito-borne diseases.
- * Current research faces challenges including data bias, language barriers, and real-world implementation difficulties.
- * Future research should expand text mining applications to include a broader range of neglected mosquito-borne diseases and refine existing methodologies.
