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Public Health Genomics and Precision Health Knowledge Base (v9.0)
PHGKB
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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-based prediction of composite risk of cardiovascular events in patients with stable angina pectoris combined with coronary heart disease: development and validation of a clinical prediction model for Chinese patients.
Zihan Wang et al. Front Pharmacol 2024 141334439
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Effective Prediction of Mortality by Heart Disease Among Women in Jordan Using the Chi-Squared Automatic Interaction Detection Model: Retrospective Validation Study.
Salam Bani Hani et al. JMIR Cardio 2023 7e48795
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A novel breakthrough in wrist-worn transdermal troponin-I-sensor assessment for acute myocardial infarction.
Shantanu Sengupta et al. Eur Heart J Digit Health 2023 4(3) 145-154
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Comparison of conventional scoring systems to machine learning models for the prediction of major adverse cardiovascular events in patients undergoing coronary computed tomography angiography.
Ghorashi Seyyed Mojtaba et al. Frontiers in cardiovascular medicine 2022 9994483
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Deep learning to detect significant coronary artery disease from plain chest radiographs AI4CAD.
D'Ancona Giuseppe et al. International journal of cardiology 2022
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A machine learning-based risk warning platform for potentially inappropriate prescriptions for elderly patients with cardiovascular disease.
Xingwei Wu et al. Frontiers in pharmacology 2022 13804566
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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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Risk Prediction of Major Adverse Cardiovascular Events Occurrence Within 6 Months After Coronary Revascularization: Machine Learning Study.
Wang Jinwan et al. JMIR medical informatics 2022 10(4) e33395
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Machine learning algorithm can provide assistance for the diagnosis of non-ST-segment elevation myocardial infarction.
Qin Lian et al. Postgraduate medical journal 2022
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Assessment of medical management in Coronary Type 2 Diabetic patients with previous percutaneous coronary intervention in Spain: A retrospective analysis of electronic health records using Natural Language Processing.
González-Juanatey Carlos et al. PloS one 2022 17(2) e0263277
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Machine learning prediction model of acute kidney injury after percutaneous coronary intervention.
Kuno Toshiki et al. Scientific reports 2022 12(1) 749
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Cluster Analysis of Cardiovascular Phenotypes in Patients With Type 2 Diabetes and Established Atherosclerotic Cardiovascular Disease: A Potential Approach to Precision Medicine.
Sharma Abhinav et al. Diabetes care 2021
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Automatic detection of vessel structure by deep learning using intravascular ultrasound images of the coronary arteries.
Shinohara Hiroki et al. PloS one 2021 16(8) e0255577
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A new approach for interpretability and reliability in clinical risk prediction: Acute coronary syndrome scenario.
Valente Francisco et al. Artificial intelligence in medicine 2021 117102113
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Machine Learning Identifies Metabolic Signatures that Predict the Risk of Recurrent Angina in Remitted Patients after Percutaneous Coronary Intervention: A Multicenter Prospective Cohort Study.
Cui Song et al. Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2021 8(10) 2003893
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Reviewing the use and quality of machine learning in developing clinical prediction models for cardiovascular disease.
Allan Simon et al. Postgraduate medical journal 2021
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Artificial Intelligence Uncovered Clinical Factors for Cardiovascular Events in Myocardial Infarction Patients with Glucose Intolerance.
Shindo Kazuhiro et al. Cardiovascular drugs and therapy 2020 May
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Integrating the STOP-BANG score and clinical data to predict cardiovascular events after infarction: A machine learning study.
Calvillo-Argüelles Oscar et al. Chest 2020 Apr
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Application of artificial neural network analysis in the evaluation of cardiovascular risk in primary Sjögren's syndrome: a novel pathogenetic scenario?
Bartoloni Elena et al. Clinical and experimental rheumatology 37 Suppl 118(3) 133-139
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Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation.
Hae Hyeonyong et al. PLoS medicine 2018 15(11) e1002693
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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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