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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 25, 2024
. (Total: 63675 Documents since 2012)
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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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Metabolic syndrome prediction using non-invasive and dietary parameters based on a support vector machine.
Sahar Mohseni-Takalloo et al. Nutr Metab Cardiovasc Dis 2023
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Development and Multinational Validation of a Novel Algorithmic Strategy for High Lp(a) Screening.
Arya Aminorroaya et al. medRxiv 2023
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Development and validation of explainable machine-learning models for carotid atherosclerosis early screening.
Ke Yun et al. J Transl Med 2023 21(1) 353
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The contribution of the exposome to the burden of cardiovascular disease.
Thomas Münzel et al. Nat Rev Cardiol 2023
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Positional sleep apnea phenotyping using machine learning and digital oximetry biomarkers.
Yuval Ben Sason et al. Physiol Meas
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An efficient approach to estimate the risk of coronary artery disease for people living with HIV using machine-learning-based retinal image analysis.
Grace Lui et al. PloS one 2023 18(2) e0281701
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Machine learning improves mortality prediction in three-vessel disease.
Xinxing Feng et al. Atherosclerosis 2023 3671-7
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Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups.
Chuan Hong et al. JAMA 2023 329(4) 306-317
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Using artificial intelligence to study atherosclerosis, predict risk and guide treatments in clinical practice.
Antoniades Charalambos et al. European heart journal 2023
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Heterogeneity in the Association Between the Presence of Coronary Artery Calcium and Cardiovascular Events: A Machine Learning Approach in the MESA Study.
Inoue Kosuke et al. Circulation 2022
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Rapid lipid-laden plaque identification in intravascular optical coherence tomography imaging based on time-series deep learning.
Rico-Jimenez Jose J et al. Journal of biomedical optics 2022 27(10)
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Metabolic syndrome screening in adolescents: New scores AI_METS based on artificial intelligence techniques.
Benmohammed Karima et al. Nutrition, metabolism, and cardiovascular diseases : NMCD 2022
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What electronic health records can and cannot tell us in the era of big data.
Bellasi Antonio et al. Atherosclerosis 2022
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A machine learning model for non-invasive detection of atherosclerotic coronary artery aneurysm.
Rostam-Alilou Ali A et al. International journal of computer assisted radiology and surgery 2022
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Machine Learning-Based Models Incorporating Social Determinants of Health vs Traditional Models for Predicting In-Hospital Mortality in Patients With Heart Failure.
Segar Matthew W et al. JAMA cardiology 2022
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Machine Learning Approach for Cardiovascular Risk and Coronary Artery Calcification Score.
Aditya C R et al. BioMed research international 2022 20222632770
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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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Causative Classification of Ischemic Stroke by the Machine Learning Algorithm Random Forests.
Wang Jianan et al. Frontiers in aging neuroscience 2022 14788637
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Prediction of 3-year all-cause and cardiovascular cause mortality in a prospective percutaneous coronary intervention registry: Machine learning model outperforms conventional clinical risk scores.
Calburean Paul-Adrian et al. Atherosclerosis 2022 35033-40
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Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review.
Suri Jasjit S et al. Metabolites 2022 12(4)
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A deep learning model for screening type 2 diabetes from retinal photographs.
Yun Jae-Seung et al. Nutrition, metabolism, and cardiovascular diseases : NMCD 2022
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Deep Phenotyping and Prediction of Long-Term Cardiovascular Disease: Optimized by Machine Learning.
Zhuang Xiao-Dong et al. The Canadian journal of cardiology 2022
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Analysis of Carotid Ultrasound Screening of High-Risk Groups of Stroke Based on Big Data Technology.
Guo Jiankang et al. Journal of healthcare engineering 2022 20226363691
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Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming?
Fotaki Anastasia et al. Frontiers in cardiovascular medicine 2022 8818765
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ECG-AI: electrocardiographic artificial intelligence model for prediction of heart failure.
Akbilgic Oguz et al. European heart journal. Digital health 2022 2(4) 626-634
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Machine Learning Approach to Classify Cardiovascular Disease in Patients With Nonalcoholic Fatty Liver Disease in the UK Biobank Cohort.
Sharma Divya et al. Journal of the American Heart Association 2021 e022576
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Machine learning of treadmill exercise test to improve selection for testing for coronary artery disease.
Lee Yin-Hao et al. Atherosclerosis 2021 34023-27
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Machine learning models for screening carotid atherosclerosis in asymptomatic adults.
Yu Jian et al. Scientific reports 2021 11(1) 22236
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 25, 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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