Catheter Ablation
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Last Posted: Sep 27, 2022
- From evidence-based medicine to digital twin technology for predicting ventricular tachycardia in ischaemic cardiomyopathy.
de Lepper Anouk G W et al. Journal of the Royal Society, Interface 2022 19(194) 20220317 - Use of digital health applications for the detection of atrial fibrillation.
Lawin Dennis et al. Herzschrittmachertherapie & Elektrophysiologie 2022 - Electrocardiographic Findings, Arrhythmias, and Left Ventricular Involvement in Familial ST-Depression Syndrome.
Christensen Alex Hørby et al. Circulation. Arrhythmia and electrophysiology 2022 101161CIRCEP121010688 - Machine learning in the detection and management of atrial fibrillation.
Wegner Felix K et al. Clinical research in cardiology : official journal of the German Cardiac Society 2022 - Association of ZFHX3 Genetic Polymorphisms and Extra-Pulmonary Vein Triggers in Patients With Atrial Fibrillation Who Underwent Catheter Ablation.
Hwang Inseok et al. Frontiers in physiology 2022 12807545 - Deep Learning-Based Recurrence Prediction of Atrial Fibrillation After Catheter Ablation.
Zhou Xue et al. Circulation journal : official journal of the Japanese Circulation Society 2021 - Association between the geographic region and the risk of familial atrioventricular nodal reentry tachycardia in the Polish population.
Deutsch Karol et al. Polish archives of internal medicine 2021 - The Clinical Application of the Deep Learning Technique for Predicting Trigger Origins in Paroxysmal Atrial Fibrillation Patients with Catheter Ablation.
Liu Chih-Min et al. Circulation. Arrhythmia and electrophysiology 2020 Oct - Prospective cross-sectional study using Poisson renewal theory to study phase singularity formation and destruction rates in atrial fibrillation (RENEWAL-AF): Study design.
Quah Jing et al. Journal of arrhythmia 2020 Aug 36(4) 660-667 - Using Machine Learning to Predict 30-Day Hospital Readmissions in Patients with Atrial Fibrillation Undergoing Catheter Ablation.
Hung Man et al. Journal of personalized medicine 2020 Aug 10(3)
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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.
- Page last reviewed:Feb 1, 2023
- Page last updated:Mar 22, 2023
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