A systematic review of text mining approaches applied to various application areas in the biomedical domain

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.

Mining netizen’s opinion on cryptocurrency: sentiment analysis of Twitter data

“…In the recent debates, text mining definition and algorithms involved in its implementation has been equivocally discussed (Hudaefi et al , 2021; Hudaefi and Badeges, 2021; Jiang et al , 2013; Villarroel Ordenes and Zhang, 2019). However, it has been understood as a specialised form of data mining to extract information and discover knowledge from vast amounts of unstructured textual data (Cheerkoot-Jalim and Khedo, 2020; Jiang et al , 2014; Justicia De La Torre et al , 2018; Usai et al , 2018). Researchers have used data mining in the area of computer science (Liu, 2012; Tsoumakas and Katakis, 2007), engineering (Hu and Liu, 2004; Medhat et al , 2014), mathematics (Berry et al , 2007; Zhao et al , 2011), social sciences (Goh et al , 2013; Liu, 2012), business and economics (Choudhury et al , 2020; Hudaefi and Badeges, 2021; Kinateder et al , 2021).…”

Guest editorial

“…Finally, the Epaminonda et al (2021) paper attempts to understand the issues at an employee (health-care) level, in a Lebanese context. The seventh paper by Cheerkoot-Jalim and Khedo (2021) is a review paper on the topic of biomedical text mining. Figure 1 below, captures the comprehensive coverage of diverse levels and contexts included in the current issue.…”

“…The papers also include a range of different methodologies applied, from qualitative (Fletcher-Brown et al , 2021; Tripathi et al , 2021: Turulja et al , 2021) to quantitative (Maheshwari et al , 2021; Arefin et al , 2021) to mixed methods (Epaminonda et al , 2021). The issue also includes a review paper on biomedical text mining (Cheerkoot-Jalim and Khedo, 2021).…”

“…Finally, last, but not the least, the Cheerkoot-Jalim and Khedo (2021) paper conducts a detailed and systematic review of literature on biomedical text mining to describe the areas of biomedical domain explored by the scholars and practitioners. The authors found that most scholars mined the data from diverse sources including electronic health records, social media and health-related forums, as well as biomedical literature.…”

Information Retrieval in an Infodemic: The Case of COVID-19 Publications

“…Since early 2020 when the pandemic was officially announced, the number of publications related to COVID-19 has had exponential growth [ 6 ]. In addition to the volume and velocity of the generated data, the heterogeneity as a result of the typical variety of concept naming found in the biomedical field, spelling mistakes, and the different source types [ 7 ] make searching and discovery of relevant literature within the COVID-19 corpora an important challenge [ 2 ].…”