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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: May 18, 2024
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Can machine learning unravel unsuspected, clinically important factors predictive of long-term mortality in complex coronary artery disease? A call for 'big data'.
Kai Ninomiya et al. Eur Heart J Digit Health 2023 4(3) 275-278
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Effect of the machine learning-derived Hypotension Prediction Index (HPI) combined with diagnostic guidance versus standard care on depth and duration of intraoperative and postoperative hypotension in elective cardiac surgery patients: HYPE-2 - study protocol of a randomised clinical trial.
Santino R Rellum et al. BMJ Open 2023 13(5) e061832
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Prediction of new onset postoperative atrial fibrillation using a simple Nomogram.
Siming Zhu et al. J Cardiothorac Surg 2023 18(1) 139
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Machine Learning to Identify Patients at Risk of Developing New-Onset Atrial Fibrillation after Coronary Artery Bypass.
Orlando Parise et al. Journal of cardiovascular development and disease 2023 10(2)
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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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Using machine learning to aid treatment decision and risk assessment for severe three-vessel coronary artery disease.
Jie Liu et al. Journal of geriatric cardiology : JGC 2022 19(5) 367-376
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Diagnostic Model of In-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction Used Artificial Intelligence Methods.
Li Yong et al. Cardiology research and practice 2022 20228758617
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Electrocardiography-Based Artificial Intelligence Algorithm Aids in Prediction of Long-term Mortality After Cardiac Surgery.
Mahayni Abdulah A et al. Mayo Clinic proceedings 2021 96(12) 3062-3070
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Predicting Postoperative Length of Stay for Isolated Coronary Artery Bypass Graft Patients Using Machine Learning.
Alshakhs Fatima et al. International journal of general medicine 2020 13751-762
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Comparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention.
Mortazavi Bobak J et al. JAMA network open 2019 Jul 2(7) e196835
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Page last reviewed:
Feb 1, 2024
Page last updated:
May 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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