Identifying the Trends of Global Publications in Health Information Technology Using Text-mining Techniques

Abstract

Background: Due to the increased publication of articles in various scientific fields, analyzing the published topics in specialized journals is important and necessary. Objectives: This research has identified the published topics in global publications in the health information technology (HIT) field. Methods: This study analyzed articles in the field of HIT using text-mining techniques. For this purpose, 162,994 documents were extracted from PubMed and Scopus databases from 2000 to 2019 using the appropriate search strategy. Text mining techniques and the Latent Dirichlet Allocation (LDA) topic modeling algorithm were used to identify the published topics. Python programming language has also been used to run text-mining algorithms. Results: This study categorized the subject of HIT-related published articles into 16 topics, the most important of which were Telemedicine and telehealth, Adoption of HIT, Radiotherapy planning techniques, Medical image analysis, and Evidence-based medicine. Conclusions: The results of the trends of subjects of HIT-related published articles represented the thematic extent and the interdisciplinary nature of this field. The publication of various topics in this scientific field has shown a growing trend in recent years.

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