Machine Learning in Cardiovascular Medicine addresses the ever-expanding applications of artificial intelligence (AI), specifically machine learning (ML), in healthcare and within cardiovascular medicine. The book focuses on emphasizing ML for biomedical applications and provides a comprehensive summary of the past and present of AI, basics of ML, and clinical applications of ML within cardiovascular medicine for predictive analytics and precision medicine. It helps readers understand how ML works along with its limitations and strengths, such that they can could harness its computational power to streamline workflow and improve patient care. It is suitable for both clinicians and engineers; providing a template for clinicians to understand areas of application of machine learning within cardiovascular research; and assist computer scientists and engineers in evaluating current and future impact of machine learning on cardiovascular medicine.

Provides an overview of machine learning, both for a clinical and engineering audience
Summarize recent advances in both cardiovascular medicine and artificial intelligence
Discusses the advantages of using machine learning for outcomes research and image processing
Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach

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Yes, you can access Machine Learning in Cardiovascular Medicine by Subhi J. Al'Aref,Gurpreet Singh,Lohendran Baskaran,Dimitri Metaxas in PDF and/or ePUB format, as well as other popular books in Scienze biologiche & Biologia. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Academic Press

Year

2020

ISBN

9780128202746

Topic

Scienze biologiche

Subtopic

Biologia

Cover image
Title page
Table of Contents
Copyright
Dedications
Contributors
Preface
Acknowledgments
Nomenclature
Introduction
Chapter 1. Technological advances within digital medicine
Chapter 2. An overview of artificial intelligence: basics and state-of-the-art algorithms
Chapter 3. Machine learning for predictive analytics
Chapter 4. Deep learning for biomedical applications
Chapter 5. Generative adversarial network for cardiovascular imaging
Chapter 6. Natural language processing
Chapter 7. Contemporary advances in medical imaging
Chapter 8. Ultrasound and artificial intelligence
Chapter 9. Computed tomography and artificial intelligence
Chapter 10. Magnetic resonance imaging and artificial intelligence
Chapter 11. Nuclear imaging and artificial intelligence
Chapter 12. Radiomics in cardiovascular imaging: principles and clinical implications
Chapter 13. Automated interpretation of electrocardiographic tracings
Chapter 14. Machine learning in cardiovascular genomics, proteomics, and drug discovery
Chapter 15. Wearable devices and machine learning algorithms for cardiovascular health assessment
Chapter 16. The future of artificial intelligence in healthcare
Chapter 17. Ethical and legal challenges
Glossary
Index