Identification of Published Topics on Ambulatory Blood Pressure Monitoring using Text Mining
Keywords:
artificial intelligence, blood pressure, text mining, ambulatory monitoring, data science, software, Cardiology.Abstract
Systemic arterial hypertension is the most prevalent disease worldwide that significantly increases cardiovascular risk. Ambulatory blood pressure monitoring allows recording readings over a 24-hour period, whether the patient is awake or asleep; it also detects occult hypertension and rules out white coat hypertension. The research aimed to identify published topics on ambulatory blood pressure monitoring, using text mining. With an R script, the Europe PMC databases were accessed; the number of publications and topics researched about ambulatory blood pressure monitoring with the descriptor "ABPM", during the period from 2010 to 2022, were requested. With the tm package was taken from text format to document, which was inspected; unneeded words and punctuations were removed, the text matrix was prepared; the most frequent elements were searched and a bar chart with the most frequent terms and word cloud was plotted with the worcloud2 package. The word cloud graph, the graph of publications per year and the word frequency graph were obtained. It was possible to identify the main topics published in the last 12 years on ambulatory blood pressure monitoring, as well as the growing interest in the subject.
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