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Last Posted: Dec 21, 2020
- Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study.
Zhu Hongling et al. The Lancet. Digital health 2020 Jul 2(7) e348-e357 - Detection of coronary artery disease in patients with chest pain: An machine learning model based on magnetocardiography parameters.
Xiao Huang et al. Clinical hemorheology and microcirculation 2020 Dec - Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation: A Population-Based Study.
Christopoulos Georgios et al. Circulation. Arrhythmia and electrophysiology 2020 Nov - Electrocardiographic predictors of infrahissian conduction disturbances in myotonic dystrophy type 1.
Joosten Isis B T 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 2020 Nov - Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial.
Hill Nathan R et al. Contemporary clinical trials 2020 Oct 106191 - Machine Learning in Electrocardiography and Echocardiography: Technological Advances in Clinical Cardiology.
Chang Amanda et al. Current cardiology reports 2020 Oct 22(12) 161 - Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model.
Li Yung-Hui et al. Sensors (Basel, Switzerland) 2020 Sep 20(19) - Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea.
Adedinsewo Demilade et al. Circulation. Arrhythmia and electrophysiology 2020 Aug 13(8) e008437 - Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL.
Strodthoff Nils et al. IEEE journal of biomedical and health informatics 2020 Sep PP - Enhancing rare variant interpretation in inherited arrhythmias through quantitative analysis of consortium disease cohorts and population controls.
Walsh Roddy et al. Genetics in medicine : official journal of the American College of Medical Genetics 2020 Sep - Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards.
Xu Haoran et al. Journal of medical systems 2020 Sep 44(10) 182 - Ajmaline Testing and the Brugada Syndrome.
Rizzo Alessandro et al. The American journal of cardiology 2020 Aug - 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 - Spectrum of transthyretin gene mutations and clinical characteristics of Polish patients with cardiac transthyretin amyloidosis.
Gawor Monika et al. Cardiology journal 2020 Aug - Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram.
Al-Zaiti Salah et al. Nature communications 2020 Aug 11(1) 3966
About HLBS-PopOmics
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 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
Content Summary
- NIH Information (2)
- COVID-19 (45)
- Human Genome Epidemiologic Studies (223)
- GWAS Studies (14)
- Human Genomics Translation/Implementation Studies (59)
- Genomic Tests Evidence Synthesis (16)
- Genomic Tests Guidelines (1)
- Non-Genomics Precision Health (45)
- State Public Health Genomics Programs (1)
- Reviews/Commentaries (10)
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Site Citation:
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:Oct 1, 2020
- Page last updated:Dec 28, 2020
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