Text mining of hypertension researches in the west Asia region: a 12-year trend analysis
Mohammad Rezapour,
Mohsen Yazdinejad,
Faezeh Rajabi Kouchi,
Masoomeh Habibi Baghi,
Zahra Khorrami,
Morteza Khavanin Zadeh,
Elmira Pourbaghi,
Hassan Rezapour
Affiliations
Mohammad Rezapour
Faculty Member of the Iranian Ministry of Science, Research and Technology, Tehran, Iran
Mohsen Yazdinejad
Artificial Intelligence, University of Isfahan, Isfahan, Iran
Faezeh Rajabi Kouchi
Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
Masoomeh Habibi Baghi
Department of Educational Science, Shahid Beheshti University, Tehran, Iran
Zahra Khorrami
Ophthalmic Epidemiology Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Morteza Khavanin Zadeh
Hasheminejad Kidney Center, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
Elmira Pourbaghi
Faculty of Advanced Sciences and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
Hassan Rezapour
Department of Transportation and Urban Infrastructure Studies, Morgan State University, Baltimore, MD, USA
More than half of the world population lives in Asia and hypertension (HTN) is the most prevalent risk factor found in Asia. There are numerous articles published about HTN in Eastern Mediterranean Region (EMRO) and artificial intelligence (AI) methods can analyze articles and extract top trends in each country. Present analysis uses Latent Dirichlet allocation (LDA) as an algorithm of topic modeling (TM) in text mining, to obtain subjective topic-word distribution from the 2790 studies over the EMRO. The period of checked studied is last 12 years and results of LDA analyses show that HTN researches published in EMRO discuss on changes in BP and the factors affecting it. Among the countries in the region, most of these articles are related to I.R Iran and Egypt, which have an increasing trend from 2017 to 2018 and reached the highest level in 2021. Meanwhile, Iraq and Lebanon have been conducting research since 2010. The EMRO word cloud illustrates ‘BMI’, ‘mortality’, ‘age’, and ‘meal’, which represent important indicators, dangerous outcomes of high BP, and gender of HTN patients in EMRO, respectively.
