ArticleIEEE journal of biomedical and health informatics2015
Big heart data: advancing health informatics through data sharing in cardiovascular imaging.
Article in IEEE journal of biomedical and health informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis.Bioengineering (Basel, Switzerland) · 2021Article
- AI in Medical Imaging Informatics: Current Challenges and Future Directions.IEEE journal of biomedical and health informatics · 2020Article
- Artificial Intelligence in Cardiac Imaging With Statistical Atlases of Cardiac Anatomy.Frontiers in cardiovascular medicine · 2020Review
- Position Paper Computational Cardiology.IEEE journal of biomedical and health informatics · 2019Article
- Biomedical Informatics on the Cloud: A Treasure Hunt for Advancing Cardiovascular Medicine.Circulation research · 2018Review
- Big data from electronic health records for early and late translational cardiovascular research: challenges and potential.European heart journal · 2018Review
- Statistical shape modeling of the left ventricle: myocardial infarct classification challenge.IEEE journal of biomedical and health informatics · 2018Article
- A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format.Journal of medical systems · 2017Article
- An SPCNN-GVF-based approach for the automatic segmentation of left ventricle in cardiac cine MR images.International journal of computer assisted radiology and surgery · 2016Article
- Cardiac image modelling: Breadth and depth in heart disease.Medical image analysis · 2016Review
- Big Data Technologies: New Opportunities for Diabetes Management.Journal of diabetes science and technology · 2015Review
- Needs assessment and prioritization of key medical specialties: A telemedicine roadmap based on a mixed-methods framework.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The burden of heart disease is rapidly worsening due to the increasing prevalence of obesity and diabetes. Data sharing and open database resources for heart health informatics are important for advancing our understanding of cardiovascular function, disease progression and therapeutics. Data sharing enables valuable information, often obtained at considerable expense and effort, to be reused beyond the specific objectives of the original study. Many government funding agencies and journal publishers are requiring data reuse, and are providing mechanisms for data curation and archival. Tools and infrastructure are available to archive anonymous data from a wide range of studies, from descriptive epidemiological data to gigabytes of imaging data. Meta-analyses can be performed to combine raw data from disparate studies to obtain unique comparisons or to enhance statistical power. Open benchmark datasets are invaluable for validating data analysis algorithms and objectively comparing results. This review provides a rationale for increased data sharing and surveys recent progress in the cardiovascular domain. We also highlight the potential of recent large cardiovascular epidemiological studies enabling collaborative efforts to facilitate data sharing, algorithms benchmarking, disease modeling and statistical atlases.
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What 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.