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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
Heart, Lung, Blood and Sleep Disorders
Last data update: Apr 23, 2024
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An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection.
Liong-Rung Liu et al. Heliyon 2024 10(5) e27200
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Artificial intelligence-enhanced 12-lead electrocardiography for identifying atrial fibrillation during sinus rhythm (AIAFib) trial: protocol for a multicenter retrospective study.
Yong-Soo Baek et al. Front Cardiovasc Med 2023 101258167
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Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023.
Yaqoob Ansari et al. Front Physiol 2023 141246746
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Prediction model of atrial fibrillation recurrence after Cox-Maze IV procedure in patients with chronic valvular disease and atrial fibrillation based on machine learning algorithm.
Zenan Jiang et al. Zhong Nan Da Xue Xue Bao Yi Xue Ban 2023 48(7) 995-1007
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Single-lead arrhythmia detection through machine learning: cross-sectional evaluation of a novel algorithm using real-world data.
Henry Mitchell et al. Open Heart 2023 10(2)
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hERG agonists pose challenges to web-based machine learning methods for prediction of drug-hERG channel interaction.
Aziza E L Harchi et al. J Pharmacol Toxicol Methods 2023 107293
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Towards Advanced Diagnosis and Management of Inherited Arrhythmia Syndromes: Harnessing the Capabilities of Artificial Intelligence and Machine Learning.
Babken Asatryan et al. Heart Rhythm 2023
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The use of digital health in heart rhythm care.
Donald P Tchapmi et al. Expert Rev Cardiovasc Ther 2023
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Use of machine learning algorithms to predict life-threatening ventricular arrhythmia in sepsis.
Le Li et al. Eur Heart J Digit Health 2023 4(3) 245-253
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Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients.
Jarne Verhaeghe et al. Int J Med Inform 2023 175105086
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Evaluation of a deep Learning-enabled automated computational heart modeling workflow for personalized assessment of ventricular arrhythmias.
Eric Sung et al. J Physiol 2023
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A descriptive study of the clinical impacts on COVID-19 survivors using telemonitoring (The TeleCOVID Study).
Josephine Sau Fan Chow et al. Frontiers in medical technology 2023 51126258
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Preterm birth and maternal heart disease: A machine learning analysis using the Korean national health insurance database.
Jue Seong Lee et al. PloS one 2023 18(3) e0283959
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Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification.
Zeynep Ozpolat et al. Diagnostics (Basel, Switzerland) 2023 13(6)
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Wearable Devices in Cardiovascular Medicine.
Andrew Hughes et al. Circulation research 2023 132(5) 652-670
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A novel artificial intelligence based algorithm to reduce wearable cardioverter-defibrillator alarms.
Jeffrey Arkles et al. Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing 2023
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Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies.
Maarten Z H Kolk et al. EBioMedicine 2023 89104462
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An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients.
Muhammad Zia Rahman et al. Computers in biology and medicine 2023 154106583
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Device agnostic AI-based analysis of ambulatory ECG recordings.
Kennedy Alan et al. Journal of electrocardiology 2022 74154-157
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Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis.
Chung Cheuk To et al. International journal of arrhythmia 2022 23(1) 24
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ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches.
Rahman Atta-Ur et al. Computational intelligence and neuroscience 2022 20226852845
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Perspectives on Sex- and Gender-Specific Prediction of New-Onset Atrial Fibrillation by Leveraging Big Data.
Geurts Sven et al. Frontiers in cardiovascular medicine 2022 9886469
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The 2021 landscape of FDA-approved artificial intelligence/machine learning-enabled medical devices: An analysis of the characteristics and intended use.
Zhu Simeng et al. International journal of medical informatics 2022 165104828
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Development of a Semi-Automated Database for Adult Congenital Heart Disease Patients.
Verma Shourya et al. The Canadian journal of cardiology 2022
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Precision Phenotyping of Dilated Cardiomyopathy Using Multidimensional Data.
Tayal Upasana et al. Journal of the American College of Cardiology 2022 79(22) 2219-2232
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Questionnaire-based exposome-wide association studies (ExWAS) reveal expected and novel risk factors associated with cardiovascular outcomes in the Personalized Environment and Genes Study.
Lee Eunice Y et al. Environmental research 2022 113463
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Bridging the Gap Between Artificial Intelligence Research and Clinical Practice in Cardiovascular Science: What the Clinician Needs to Know.
Shipley Emily et al. Arrhythmia & electrophysiology review 2022 11e03
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Generalizable Beat-by-Beat Arrhythmia Detection by Using Weakly Supervised Deep Learning.
Liu Yang et al. Frontiers in physiology 2022 13850951
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Machine learning techniques for arrhythmic risk stratification: a review of the literature.
Chung Cheuk To et al. International journal of arrhythmia 2022 23
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Mobile health technology in atrial fibrillation.
Bonini Niccolò et al. Expert review of medical devices 2022 1-14
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 23, 2024
Content source:
Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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National Center on Birth Defects and Developmental Disabilities
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