Approaches for text mining of mHealth literature.

© mHealth. All rights reserved. mHealth 2022;8:11 | https://dx.doi. org/10.21037/mhealth-22-1 As the scientific literature search is becoming increasingly difficult to perform manually, as they do in the other areas of scientific discovery and improvement, computers are helping us automate this task too. mHealth is a relatively young research domain with an accelerated rate of scientific literature productivity, especially since mid-2010s (1). In 2017, we captured the time dependency of terminology, clusters of mHealth literature, and their trend analyses using text mining techniques (2). In 2019, Hyejin Park and Min Sook Park have followed-up with a paper that used text mining techniques to specifically study medical conditions, interventions, and study populations and medical conditions’ relationships with categories of interventions and study populations (3). In this paper, we discuss the methods and results of the two studies and how future work can improve use of text mining to capture trends in mHealth research in light of our experiences, other literature that used text mining for surveying the literature, and the evolution of text mining methods for literature review. One of the initial steps of text mining of the literature is the search of databases to identify as many of the relevant publications as possible (establishing the corpus). This is an important step for any literature survey study; however, especially important for text mining studies, compared to manual review of the literature, because text mining methods are more accurate when there are larger data. When this work is done for a relatively younger field, where there are fewer years with research dissemination, and a field that is not strictly confined in one discipline, the impact of searching a higher number of databases is more significant in the interpretation of the results. mHealth is both a relatively younger research field and an interdisciplinary one. Furthermore, scientific literature databases may be explicitly or implicitly biased for one discipline over another, and their coverage of journals is not all-inclusive (4). Therefore, although databases such as Web of Science and Scopus are considered more interdisciplinary than databases such as PubMed and IEEE Xplore, incorporating articles from multiple databases vs. using a single interdisciplinary database would significantly impact the interpretation of results (5,6). Of course, the inclusion of articles from multiple databases introduces the problem of retrieving duplicate citations; however, most citation management tools help deal with the duplicate records issue relatively easily. In cases where the features of these tools are not sufficient, a manual duplication removal method was described in our earlier work (2). In terms of database searches, the use of search terms and the inclusion of publication types are other important considerations. We feel that providing details of the determination of search terms in a text mining publication is important for reproducibility and clarity of the process authors go through establishing the final search string. For example, inclusion of some conference publications should be considered in mHealth literature survey because disciplines like computer science that are relevant for mHealth research put a relatively much higher value on conference publications compared to other disciplines. Similar to a detailed explanation of the searching process for establishing the corpus, explaining the process of curating the term list through tokenizing, phrasing, and terming in detail is also important for the purposes of reproducibility. Finally, publishing the list of articles and the final corpus used for analyses to accompany the manuscript would improve the Editorial