ArticleHealthcare informatics research2025
Integrating Large-Scale Data Analytics for Cardiovascular Disease Prediction: A Scoping Review.
Article in Healthcare informatics research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Perception and Knowledge About Telenursing and eHealth Among Nursing University Students: A Cross-Sectional Study in the United Arab Emirates.Public health nursing (Boston, Mass.)Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis scoping review synthesizes literature on the integration of large-scale data analytics for cardiovascular disease (CVD) prediction, aiming to provide insights that support the adoption of predictive analytics for improved prevention and early detection in healthcare.
methodsSearches were conducted in Medline (PubMed), EBSCO, Google Scholar, and Wiley Online Library. Medical Subject Headings (MeSH) search terms included: large-scale data, big data, cardiovascular diseases, prediction, machine-learning algorithms, artificial intelligence, and mortality. The search covered the period from 2020 to 2024.
resultsOf 262 retrieved articles, 16 were included. Three main themes were identified: large-scale data analysis techniques and machine-learning algorithms; applications of machine-learning algorithms and artificial intelligence in predicting cardiovascular diseases; and the role of integrating large-scale data in disease prediction to improve the quality of care.
conclusionsWhile machine learning provides considerable opportunities for predicting CVD outcomes, limitations remain. Machine-learning approaches are not always the most appropriate option, particularly in basic research where causal relationships between variables may be more critical than optimized predictions. To ensure fair and effective healthcare outcomes, issues related to bias, data quality, ethical concerns, and practical implementation must be addressed. Overcoming these challenges will require interdisciplinary collaboration, methodological refinement, and further research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.