TY  - JOUR
AU  - Abba, Mustapha
AU  - Nduka, Chidozie
AU  - Anjorin, Seun
AU  - Mohamed, Shukri
AU  - Agogo, Emmanuel
AU  - Uthman, Olalekan
PY  - 2022
DA  - 2022/5/18
TI  - One Hundred Years of Hypertension Research: Topic Modeling Study
JO  - JMIR Form Res
SP  - e31292
VL  - 6
IS  - 5
KW  - hypertension
KW  - high blood pressure
KW  - machine learning
KW  - topic modeling
KW  - latent Dirichlet allocation
KW  - LDA
KW  - cardiovascular
KW  - research trends
AB  - Background: Due to scientific and technical advancements in the field, published hypertension research has developed substantially during the last decade. Given the amount of scientific material published in this field, identifying the relevant information is difficult. We used topic modeling, which is a strong approach for extracting useful information from enormous amounts of unstructured text. Objective: This study aims to use a machine learning algorithm to uncover hidden topics and subtopics from 100 years of peer-reviewed hypertension publications and identify temporal trends. Methods: The titles and abstracts of hypertension papers indexed in PubMed were examined. We used the latent Dirichlet allocation model to select 20 primary subjects and then ran a trend analysis to see how popular they were over time. Results: We gathered 581,750 hypertension-related research articles from 1900 to 2018 and divided them into 20 topics. These topics were broadly categorized as preclinical, epidemiology, complications, and therapy studies. Topic 2 (evidence review) and topic 19 (major cardiovascular events) are the key (hot topics). Most of the cardiopulmonary disease subtopics show little variation over time, and only make a small contribution in terms of proportions. The majority of the articles (414,206/581,750; 71.2%) had a negative valency, followed by positive (119, 841/581,750; 20.6%) and neutral valency (47,704/581,750; 8.2%). Between 1980 and 2000, negative sentiment articles fell somewhat, while positive and neutral sentiment articles climbed substantially. Conclusions: The number of publications has been increasing exponentially over the period. Most of the uncovered topics can be grouped into four categories (ie, preclinical, epidemiology, complications, and treatment-related studies). 
SN  - 2561-326X
UR  - https://formative.jmir.org/2022/5/e31292
UR  - https://doi.org/10.2196/31292
UR  - http://www.ncbi.nlm.nih.gov/pubmed/35583933
DO  - 10.2196/31292
ID  - info:doi/10.2196/31292
ER  -