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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 18, 2024
. (Total: 63623 Documents since 2012)
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Assessing Biological Age: The Potential of ECG Evaluation Using Artificial Intelligence: JACC Family Series.
Francisco Lopez-Jimenez et al. JACC Clin Electrophysiol 2024
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Non-Invasive Heart Failure Evaluation Using Machine Learning Algorithms.
Odeh Adeyi Victor et al. Sensors (Basel) 2024 24(7)
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Deep learning evaluation of echocardiograms to identify occult atrial fibrillation.
Neal Yuan et al. NPJ Digit Med 2024 7(1) 96
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A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms.
Utkarsh Gupta et al. Heliyon 2024 10(5) e26787
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Utilizing ultra-early continuous physiologic data to develop automated measures of clinical severity in a traumatic brain injury population.
Shiming Yang et al. Sci Rep 2024 14(1) 7618
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Artificial intelligence-enabled prediction of chemotherapy-induced cardiotoxicity from baseline electrocardiograms.
Ryuichiro Yagi et al. Nat Commun 2024 15(1) 2536
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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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Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model.
Jingjing Fan et al. Stress Health 2024 e3386
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An ECG-based artificial intelligence model for assessment of sudden cardiac death risk
L Holmstrom et al, Comm Med, February 2024
Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images with Deep Learning.
Veer Sangha et al. medRxiv 2024
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Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification.
Khaled Abdelrahman et al. Diagnostics (Basel) 2024 14(2)
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A generalizable electrocardiogram-based artificial intelligence model for 10-year heart failure risk prediction.
Liam Butler et al. Cardiovasc Digit Health J 2024 4(6) 183-190
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Opportunistic Screening for Asymptomatic Left Ventricular Dysfunction Using Electrocardiographic Artificial Intelligence: A Cost-Effective Approach.
Wei-Ting Liu et al. Can J Cardiol 2023
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Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG.
Samir Awasthi et al. EClinicalMedicine 2023 65102259
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Single-lead ECG AI model with risk factors detects Atrial Fibrillation during Sinus Rhythm.
Stijn Dupulthys et al. Europace 2023
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Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias
M Gadaleta et al, NPJ Digital Medicine, December 12, 2023
A Deep-Learning Algorithm-Enhanced Electrocardiogram Interpretation for Detecting Pulmonary Embolism.
Yu-Cheng Chen et al. Acta Cardiol Sin 2023 39(6) 913-928
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Feasibility and limitations of deep learning-based coronary calcium scoring in PET-CT: a comparison with coronary calcium score CT.
Hee Sang Oh et al. Eur Radiol 2023
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Deep learning-based NT-proBNP prediction from the ECG for risk assessment in the community.
Meraj Neyazi et al. Clin Chem Lab Med 2023
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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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Deep learning-based model detects atrial septal defects from electrocardiography: a cross-sectional multicenter hospital-based study.
Kotaro Miura et al. EClinicalMedicine 2023 63102141
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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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A Systematic Review: Do the Use of Machine Learning, Deep Learning, and Artificial Intelligence Improve Patient Outcomes in Acute Myocardial Ischemia Compared to Clinician-Only Approaches?
Binay K Panjiyar et al. Cureus 2023 15(8) e43003
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Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network.
Mohammad Mahbubur Rahman Khan Mamun et al. Bioengineering (Basel) 2023 10(7)
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Externally validated deep learning model to identify prodromal Parkinson's disease from electrocardiogram.
Ibrahim Karabayir et al. Sci Rep 2023 13(1) 12290
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An AI-Enabled Dynamic Risk Stratification for Emergency Department Patients with ECG and CXR Integration.
Yu-Hsuan Jamie Chen et al. J Med Syst 2023 47(1) 81
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Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices.
Akshay Khunte et al. NPJ Digit Med 2023 6(1) 124
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Cardiovascular disease/stroke risk stratification in deep learning framework: a review.
Mrinalini Bhagawati et al. Cardiovasc Diagn Ther 2023 13(3) 557-598
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Using Artificial Intelligence in Predicting Ischemic Stroke Events After Percutaneous Coronary Intervention.
Chieh-Ju Chao et al. J Invasive Cardiol 2023 35(6) E297-E311
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 18, 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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