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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Last data update: Jun 19, 2024
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Development and validation of a machine learning-based readmission risk prediction model for non-ST elevation myocardial infarction patients after percutaneous coronary intervention.
Yanxu Liu et al. Sci Rep 2024 14(1) 13393
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The potential of virtual triage AI to improve early detection, care acuity alignment, and emergent care referral of life-threatening conditions.
George A Gellert et al. Front Public Health 2024 121362246
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Development and validation of risk prediction model for recurrent cardiovascular events among Chinese: the Personalized CARdiovascular DIsease risk Assessment for Chinese model.
Yekai Zhou et al. Eur Heart J Digit Health 2024 5(3) 363-370
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The use of artificial intelligence for predicting postinfarction myocardial viability in echocardiographic images.
Blazej Michalski et al. Cardiol J 2024
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Physical performance strongly predicts all-cause mortality risk in a real-world population of older diabetic patients: machine learning approach for mortality risk stratification.
Alberto Montesanto et al. Front Endocrinol (Lausanne) 2024 151359482
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1-Year Mortality Prediction through Artificial Intelligence Using Hemodynamic Trace Analysis among Patients with ST Elevation Myocardial Infarction.
Seyed Reza Razavi et al. Medicina (Kaunas) 2024 60(4)
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Machine learning to identify a composite indicator to predict cardiac death in ischemic heart disease.
Alessandro Pingitore et al. Int J Cardiol 2024 131981
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Using the Super Learner algorithm to predict risk of major adverse cardiovascular events after percutaneous coronary intervention in patients with myocardial infarction.
Xiang Zhu et al. BMC Med Res Methodol 2024 24(1) 59
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Digital Health for Myocardial Infarction: Research Topics and Trends.
Melissa Pelly et al. Stud Health Technol Inform 2024 310429-433
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Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study.
Pin Zhang et al. JMIR Form Res 2024 8e48487
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Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis: a population-based study.
Yicheng Wang et al. Front Cardiovasc Med 2023 101296405
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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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Machine learning for predicting intrahospital mortality in ST-elevation myocardial infarction patients with type 2 diabetes mellitus.
Panke Chen et al. BMC Cardiovasc Disord 2023 23(1) 585
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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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Predicting Major Adverse Cardiovascular Events Following Carotid Endarterectomy Using Machine Learning.
Ben Li et al. J Am Heart Assoc 2023 e030508
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Comparison of machine-learning models for the prediction of 1-year adverse outcomes of patients undergoing primary percutaneous coronary intervention for acute ST-elevation myocardial infarction.
Saeed Tofighi et al. Clin Cardiol 2023
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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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Combinatorial Use of Machine Learning and Logistic Regression for Predicting Carotid Plaque Risk Among 5.4 Million Adults With Fatty Liver Disease Receiving Health Check-Ups: Population-Based Cross-Sectional Study.
Yuhan Deng et al. JMIR Public Health Surveill 2023 9e47095
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A foundation model for generalizable disease detection from retinal images.
Yukun Zhou et al. Nature 2023
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Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction.
Constantine Tarabanis et al. Cardiovasc Digit Health J 2023 4(4) 126-132
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Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction.
Salah S Al-Zaiti et al. Nat Med 2023
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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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Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease.
Seong Huan Choi et al. BMC Cardiovasc Disord 2023 23(1) 287
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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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Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations.
Dimitrios Doudesis et al. Nat Med 2023
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Personalized diagnosis in suspected myocardial infarction.
Johannes Tobias Neumann et al. Clin Res Cardiol 2023
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Machine learning prediction of mortality in Acute Myocardial Infarction.
Mariana Oliveira et al. BMC Med Inform Decis Mak 23(1) 70
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Integrating multimodal information in machine learning for classifying acute myocardial infarction.
Ran Xiao et al. Physiological measurement 2023
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SERS spectroscopy with machine learning to analyze human plasma derived sEVs for coronary artery disease diagnosis and prognosis.
Xi Huang et al. Bioengineering & translational medicine 2023 8(2) e10420
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Page last reviewed:
Feb 1, 2024
Page last updated:
Jun 19, 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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