Gaussian Process-based Active Learning for Efficient Cardiovascular Disease Inference-Reference-Cited by-同舟云学术

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Gaussian Process-based Active Learning for Efficient Cardiovascular Disease Inference

Published:2023-12-05 Issue: Volume:35 Page:3188-3193
ISSN:
Container-title:2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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Author:

Tassi Stavroula C.¹,Polyzos Konstantinos D.²,Fotiadis Dimitrios I.¹,Sakellarios Antonis I.³

Affiliation:

1. University of Ioannina,Unit of Medical Technology and Intelligent Information Systems (MEDLAB),Department of Material Science and Engineering,Ioannina,Greece

2. University of Minnesota,Department of Electrical and Computer Engineering,Minnesota,USA

3. University of Patras,Laboratory of Biomechanics and Biomedical Engineering,Department of Mechanical and Aeronautics Engineering,Patras,Greece

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10385250/10385251/10385861.pdf?arnumber=10385861

Reference33 articles.

1. SmartFFR, a New Functional Index of Coronary Stenosis: Comparison With Invasive FFR Data

2. Clinical outcomes of fractional flow reserve by computed tomographic angiography-guided diagnostic strategies vs. usual care in patients with suspected coronary artery disease: the prospective longitudinal trial of FFRCT: outcome and resource impacts study

3. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants

4. Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques

5. Cardiovascular diseases prediction by machine learning incorporation with deep learning

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