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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
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Official URL: https://doi.org/10.2196/31292
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 | ||||||||||||
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| Subjects: | Q Science > Q Science (General) R Medicine > RC Internal medicine |
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| Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
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| 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: |
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| Volume: | 6 | ||||||||||||
| Number: | 5 | ||||||||||||
| Article Number: | e31292 | ||||||||||||
| DOI: | 10.2196/31292 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Access rights to Published version: | Open Access | ||||||||||||
| RIOXX Funder/Project Grant: |
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