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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Public Health Genomics Branch
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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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Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD.
Ben Li et al. JAMA Netw Open 2024 7(3) e242350
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Cardiovascular disease (CVD) outcomes and associated risk factors in a medicare population without prior CVD history: an analysis using statistical and machine learning algorithms.
Gregory Yoke Hong Lip et al. Intern Emerg Med 2023 1-11
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Wearable Devices in Cardiovascular Medicine.
Andrew Hughes et al. Circulation research 2023 132(5) 652-670
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Using a cohort study of diabetes and peripheral artery disease to compare logistic regression and machine learning via random forest modeling.
Austin Andrea M et al. BMC medical research methodology 2022 22(1) 300
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Risk stratification with explainable machine learning for 30-day procedure-related mortality and 30-day unplanned readmission in patients with peripheral arterial disease.
Cox Meredith et al. PloS one 2022 17(11) e0277507
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Machine Learning Approach to Predict In-Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States.
Zhang Donglan et al. Journal of the American Heart Association 2022 e026987
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Applications of artificial intelligence for patients with peripheral artery disease.
Lareyre Fabien et al. Journal of vascular surgery 2022
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Artificial intelligence for the evaluation of peripheral artery disease using arterial Doppler waveforms to predict abnormal ankle-brachial index.
McBane Robert D et al. Vascular medicine (London, England) 2022 1358863X221094082
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Interpretable Machine Learning for the Prediction of Amputation Risk Following Lower Extremity Infrainguinal Endovascular Interventions for Peripheral Arterial Disease.
Cox Meredith et al. Cardiovascular and interventional radiology 2022
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Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters.
Sonnenschein Kristina et al. Biomedicines 2021 9(10)
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Machine-Learning-Derived Model for the Stratification of Cardiovascular risk in Patients with Ischemic Stroke.
Ntaios George et al. Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association 2021 30(10) 106018
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Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data.
Ross Elsie Gyang et al. Circulation. Cardiovascular quality and outcomes 2019 12(3) e004741
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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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