Last Posted: Mar 18, 2023
- Generalizable and robust deep learning algorithm for atrial fibrillation diagnosis across geography, ages and sexes.
Shany Biton et al. NPJ digital medicine 2023 3 (1) 44
- An artificial intelligence-based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening.
Tove Hygrell et al. Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology 2023
- Association of accelerometer-derived circadian abnormalities and genetic risk with incidence of atrial fibrillation
L Yang et al, NPJ Digital Medicine, March 4, 2023
- Global research trends of hypertrophic cardiomyopathy from 2000 to 2022: Insights from bibliometric analysis.
Xifeng Zheng et al. Frontiers in cardiovascular medicine 2023 101039098
- Machine Learning to Identify Patients at Risk of Developing New-Onset Atrial Fibrillation after Coronary Artery Bypass.
Orlando Parise et al. Journal of cardiovascular development and disease 2023 10(2)
- Value of baseline characteristics in the risk prediction of atrial fibrillation.
Jiacheng He et al. Frontiers in cardiovascular medicine 2023 101068562
- Investigating genes associated with heart failure, atrial fibrillation, and other cardiovascular diseases, and predicting disease using machine learning techniques for translational research and precision medicine.
Vignesh Venkat et al. Genomics 2023 110584
- Prediabetes as a risk factor for new-onset atrial fibrillation: the propensity-score matching cohort analyzed using the Cox regression model coupled with the random survival forest.
Jung-Chi Hsu et al. Cardiovascular diabetology 2023 22(1) 35
- Social Determinants, Cardiovascular Disease, and Health Care Cost: A Nationwide Study in the United States Using Machine Learning.
Feinuo Sun et al. Journal of the American Heart Association 2023 e027919
- Genetics of atrial fibrillation.
David S M Lee et al. Current opinion in cardiology 2023
HLBS-PopOmics is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that address the translation of genomic and other precision health discoveries into improved health care and prevention related to Heart and Vascular Diseases(H), Lung Diseases(L), Blood Diseases(B), and Sleep Disorders(S)...more
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Mensah GA, Yu W, Barfield WL, Clyne M, Engelgau MM, Khoury MJ. HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders. Genet Med. 2018 Sep 10. doi: 10.1038/s41436-018-0118-1
Disclaimer: Articles listed in the Public Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
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- Page last updated:Mar 30, 2023
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