ReviewJournal of cardiovascular translational research2020
Big Data and Atrial Fibrillation: Current Understanding and New Opportunities.
Review in Journal of cardiovascular translational research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Bidirectional effects of oral anticoagulants on gut microbiota in patients with atrial fibrillation.Frontiers in cellular and infection microbiology · 2023Article
- Research output of artificial intelligence in arrhythmia from 2004 to 2021: a bibliometric analysis.Journal of thoracic disease · 2022Article
- Article
- Atrial fibrillation risk prediction from the 12-lead electrocardiogram using digital biomarkers and deep representation learning.European heart journal. Digital health · 2021Article
- Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care.Cardiovascular research · 2021Review
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
Atrial fibrillation (AF) is the most common arrhythmia with diverse etiology that remarkably relates to high morbidity and mortality. With the advancements in intensive clinical and basic research, the understanding of electrophysiological and pathophysiological mechanism, as well as treatment of AF have made huge progress. However, many unresolved issues remain, including the core mechanisms and key intervention targets. Big data approach has produced new insights into the improvement of the situation. A large amount of data have been accumulated in the field of AF research, thus using the big data to achieve prevention and precise treatment of AF may be the direction of future development. In this review, we will discuss the current understanding of big data and explore the potential applications of big data in AF research, including predictive models of disease processes, disease heterogeneity, drug safety and development, precision medicine, and the potential source for big data acquisition. Grapical abstract.
Indexed as
Identifiers
32378163What OpenQuestion holds
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.