Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 May 18;6(5):e31292.
doi: 10.2196/31292.

One Hundred Years of Hypertension Research: Topic Modeling Study

Mustapha Abba #  1 , Chidozie Nduka  1 , Seun Anjorin  1 , Shukri Mohamed #  1 , Emmanuel Agogo  2 , Olalekan Uthman  1
Affiliations

One Hundred Years of Hypertension Research: Topic Modeling Study

Mustapha Abba et al. JMIR Form Res. .

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).

Keywords: LDA; cardiovascular; high blood pressure; hypertension; latent Dirichlet allocation; machine learning; research trends; topic modeling.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Trends in the number of hypertension research indexed in PubMed, 1900 to 2018.
Figure 2
Figure 2
The clustering topics.
Figure 3
Figure 3
Dynamics and trends of the topics.
Figure 4
Figure 4
Dynamics and trends of the article valency.
See this image and copyright information in PMC

References

    1. Devos P, Menard J. Bibliometric analysis of research relating to hypertension reported over the period 1997-2016. J Hypertens. 2019 Nov;37(11):2116–2122. doi: 10.1097/HJH.0000000000002143. http://europepmc.org/abstract/MED/31136459 - DOI - PMC - PubMed
    1. Mills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nat Rev Nephrol. 2020 Apr;16(4):223–237. doi: 10.1038/s41581-019-0244-2. http://europepmc.org/abstract/MED/32024986 10.1038/s41581-019-0244-2 - DOI - PMC - PubMed
    1. Carey RM, Muntner P, Bosworth HB, Whelton PK. Prevention and control of hypertension. J Am Coll Cardiol. 2018 Sep;72(11):1278–1293. doi: 10.1016/J.JACC.2018.07.008. - DOI - PMC - PubMed
    1. A global brief on hypertension: Silent killer, global public health crisis. World Health Organisation. 2013. [2020-12-20]. https://www.who.int/publications/i/item/a-global-brief-on-hypertension-s... .
    1. Koren G, Nordon G, Radinsky K, Shalev V. Machine learning of big data in gaining insight into successful treatment of hypertension. Pharmacol Res Perspect. 2018 Jun;6(3):e00396. doi: 10.1002/prp2.396.PRP2396 - DOI - PMC - PubMed