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One hundred years of hypertension research : a topic modelling study

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Abba, Mustapha S., Nduka, Chidozie U., Anjorin, Seun S., Mohamed, Shukri F., Agogo, Emmanuel and Uthman, Olalekan A. (2022) One hundred years of hypertension research : a topic modelling study. JMIR Formative Research, 6 (5). e31292. doi:10.2196/31292 ISSN 2561-326X.

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Official URL: https://doi.org/10.2196/31292

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Abstract

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).

Item Type: Journal Article
Subjects: Q Science > Q Science (General)
R Medicine > RC Internal medicine
Divisions: Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences
Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Hypertension , Hypertension -- Research -- Mathematical models , Machine learning
Journal or Publication Title: JMIR Formative Research
Publisher: JMIR Publications Inc.
ISSN: 2561-326X
Official Date: 18 May 2022
Dates:
DateEvent
18 May 2022Published
22 April 2022Accepted
Volume: 6
Number: 5
Article Number: e31292
DOI: 10.2196/31292
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 16 June 2022
Date of first compliant Open Access: 17 June 2022
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDPetroleum Technology Development Fundhttp://dx.doi.org/10.13039/501100009614
UNSPECIFIED[NIHR] National Institute for Health Researchhttp://dx.doi.org/10.13039/501100000272
UNSPECIFIEDWellcome Trusthttp://dx.doi.org/10.13039/100010269

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