A systematic review of text mining approaches applied to various application areas in the biomedical domain. Issue 3 (21st December 2020)

• Record Type: Journal Article
• Authors: Cheerkoot-Jalim, Sudha; Khedo, Kavi Kumar
• Is Part Of: Journal of knowledge management. Volume 25:Issue 3(2021)
• Page Start: 642
• Page End: 668
• Publication Date: 2020-12-21
• Subjects: Biomedical literature -- Biomedical text mining -- Kitchenham methodology -- Text mining Techniques -- Text mining tools; Knowledge management -- Periodicals; 658.4
• DOI: 10.1108/JKM-09-2019-0524
• Languages: English
• ISSNs: 1367-3270

Abstract

Abstract : Purpose: This work shows the results of a systematic literature review on biomedical text mining. The purpose of this study is to identify the different text mining approaches used in different application areas of the biomedical domain, the common tools used and the challenges of biomedical text mining as compared to generic text mining algorithms. This study will be of value to biomedical researchers by allowing them to correlate text mining approaches to specific biomedical application areas. Implications for future research are also discussed. Design/methodology/approach: The review was conducted following the principles of the Kitchenham method. A number of research questions were first formulated, followed by the definition of the search strategy. The papers were then selected based on a list of assessment criteria. Each of the papers were analyzed and information relevant to the research questions were extracted. Findings: It was found that researchers have mostly harnessed data sources such as electronic health records, biomedical literature, social media and health-related forums. The most common text mining technique was natural language processing using tools such as MetaMap and Unstructured Information Management Architecture, alongside the use of medical terminologies such as Unified Medical Language System. The main application area was the detection of adverse drug events. Challenges identified included the need to deal with huge amounts of text, the heterogeneity of the different data sources, the duality of meaning of words in biomedical text and the amount of noise introduced mainly from social media and health-related forums. Originality/value: To the best of the authors' knowledge, other reviews in this area have focused on either specific techniques, specific application areas or specific data sources. The results of this review will help researchers to correlate most relevant and recent advances in text mining approaches to specific biomedical application areas by providing an up-to-date and holistic view of work done in this research area. The use of emerging text mining techniques has great potential to spur the development of innovative applications, thus considerably impacting on the advancement of biomedical research.