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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)
Last data update: Apr 30, 2024
. (Total: 63724 Documents since 2012)
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Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound.
Evangelos K Oikonomou et al. medRxiv 2024
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Electrocardiography deep-learning models to predict high-risk Imaging features in Patients with Hypertrophic Cardiomyopathy: Can It Change Clinical Practice?
Ghassan Bidaoui et al. Heart Rhythm 2024
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Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images with Deep Learning.
Veer Sangha et al. medRxiv 2024
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Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives.
Xiaoyu Sun et al. Eur J Med Res 2023 28(1) 242
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Collinearity and Dimensionality Reduction in Radiomics: Effect of Preprocessing Parameters in Hypertrophic Cardiomyopathy Magnetic Resonance T1 and T2 Mapping.
Chiara Marzi et al. Bioengineering (Basel, Switzerland) 2023 10(1)
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Tandem deep learning and logistic regression models to optimize hypertrophic cardiomyopathy detection in routine clinical practice.
Maanja Maren et al. Cardiovascular digital health journal 2023 3(6) 289-296
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Identification of fibrosis in hypertrophic cardiomyopathy: a radiomic study on cardiac magnetic resonance cine imaging.
Pu Cailing et al. European radiology 2022
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Predictors of Adherence to Stroke Prevention in the BALKAN-AF Study: A Machine-Learning Approach.
Koziel-Siolkowska Monika et al. TH open : companion journal to thrombosis and haemostasis 2022 6(3) e283-e290
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Artificial Intelligence Applied to Cardiomyopathies: Is It Time for Clinical Application?
Kim Kyung-Hee et al. Current cardiology reports 2022
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Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy.
Fahmy Ahmed S et al. Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance 2022 24(1) 40
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High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning.
Duffy Grant et al. JAMA cardiology 2022
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Artificial intelligence study on left ventricular function among normal individuals, hypertrophic cardiomyopathy and dilated cardiomyopathy patients using 1.5T cardiac cine MR images obtained by SSFP sequence.
Guo Jiajun et al. The British journal of radiology 2022 20201060
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Explanatory Analysis of a Machine Learning Model to Identify Hypertrophic Cardiomyopathy Patients from EHR Using Diagnostic Codes.
Farahani Nasibeh Zanjirani et al. Proceedings. IEEE International Conference on Bioinformatics and Biomedicine 2021 20201932-1937
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A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy.
Smole Tim et al. Computers in biology and medicine 2021 135104648
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Machine Learning Methods for Identifying Atrial Fibrillation Cases and Their Predictors in Patients With Hypertrophic Cardiomyopathy: The HCM-AF-Risk Model.
Bhattacharya Moumita et al. CJC open 2021 3(6) 801-813
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A radiomic approach to predict myocardial fibrosis on coronary CT angiography in hypertrophic cardiomyopathy.
Qin Le et al. International journal of cardiology 2021
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Neural-Network-Based Diagnosis Using 3-Dimensional Myocardial Architecture and Deformation: Demonstration for the Differentiation of Hypertrophic Cardiomyopathy.
Satriano Alessandro et al. Frontiers in cardiovascular medicine 2020 7584727
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Deep learning algorithm to improve hypertrophic cardiomyopathy mutation prediction using cardiac cine images.
Zhou Hongyu et al. European radiology 2020 Nov
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Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy (HCM-VAr-Risk Model).
Bhattacharya Moumita et al. The American journal of cardiology 2019 123(10) 1681-1689
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Machine learning detection of obstructive hypertrophic cardiomyopathy using a wearable biosensor
R Ueno et al, NPJ Digital Medicine, December 2019
Three-dimensional Deep Convolutional Neural Networks for Automated Myocardial Scar Quantification in Hypertrophic Cardiomyopathy: A Multicenter Multivendor Study.
Fahmy Ahmed S et al. Radiology 2019 Nov 190737
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Machine learning detection of obstructive hypertrophic cardiomyopathy using a wearable biosensor.
Green Eric M, et al. NPJ digital medicine 2019 0 0. 57
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 30, 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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