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One Hundred Years of Hypertension Research: Topic Modeling Study.
Mustapha Abba1, Chidozie Nduka1, Seun Anjorin1
1Warwick Centre for Global Health, Division of Health Sciences, University of Warwick Medical School, University of Warwick, Coventry, United Kingdom.
JMIR Formative Research
|May 18, 2022
Summary
This study used topic modeling on 581,750 hypertension articles to identify 20 research topics and their trends over 100 years. Key topics included evidence reviews and cardiovascular events, with sentiment shifting towards positive over time.
Area of Science:
- Cardiovascular research
- Bibliometrics
- Machine learning in science
Background:
- Hypertension research has grown significantly, making information retrieval challenging.
- Topic modeling offers a robust method for extracting insights from large volumes of scientific text.
- Advancements in technology necessitate new approaches to analyze extensive research data.
Purpose of the Study:
- To apply machine learning for uncovering hidden topics and subtopics in hypertension literature.
- To analyze temporal trends in hypertension research over a 100-year period.
- To identify emerging and declining areas of focus within hypertension studies.
Main Methods:
- Utilized latent Dirichlet allocation (LDA) topic modeling on 581,750 PubMed-indexed hypertension articles (1900-2018).
- Extracted 20 primary topics and performed trend analysis to assess their popularity over time.
- Categorized topics into preclinical, epidemiology, complications, and therapy.
Main Results:
- Identified 20 distinct research topics, with 'evidence review' and 'major cardiovascular events' being prominent.
- Observed shifts in sentiment over time, with a decrease in negative and an increase in positive/neutral articles from 1980-2000.
- Cardiopulmonary disease subtopics showed minimal temporal variation and contribution.
Conclusions:
- Publication counts in hypertension research show exponential growth.
- The identified topics broadly align with preclinical, epidemiological, complications, and treatment research categories.
- Machine learning effectively reveals thematic structures and temporal dynamics in large scientific corpora.
Keywords:
LDAcardiovascularhigh blood pressurehypertensionlatent Dirichlet allocationmachine learningresearch trendstopic modelingMore Related Videos
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