Machine Learning in Cardiovascular Medicine
Editat de Subhi J. Al'Aref, Gurpreet Singh, Lohendran Baskaran, Dimitri Metaxasen Limba Engleză Paperback – 26 noi 2020
- 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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Specificații
ISBN-13: 9780128202739
ISBN-10: 0128202734
Pagini: 454
Ilustrații: 350 illustrations (300 in full color)
Dimensiuni: 191 x 235 x 24 mm
Greutate: 0.93 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0128202734
Pagini: 454
Ilustrații: 350 illustrations (300 in full color)
Dimensiuni: 191 x 235 x 24 mm
Greutate: 0.93 kg
Editura: ELSEVIER SCIENCE
Public țintă
Cardiovascular researchers, practicing clinicians, and engineers engaged in biomedical research. Computer ScientistsCuprins
1. Technological Advances within Digital Medicine
2. An Overview of Artificial Intelligence: Basics and State-of-the-Art Algorithms
3. Machine Learning for Predictive Analytics
4. Deep Learning for Biomedical Applications
5. Generative Adversarial Network for Cardiovascular Imaging
6. Natural Language Processing
7. Contemporary Advances in Medical Imaging
8. Ultrasound and Artificial Intelligence
9. Computed Tomography and Artificial Intelligence
10. Magnetic Resonance Imaging and Artificial Intelligence
11. Nuclear Imaging and Artificial Intelligence
12. Radiomics in Cardiovascular Imaging: Principles and Clinical Implications
13. Automated Interpretation of Electrocardiographic Tracings
14. Machine Learning in Cardiovascular Genomics, Proteomics, and Drug Discovery
15. Wearable Devices and Machine Learning Algorithms for Cardiovascular Health Assessment
16. The Future of Artificial Intelligence in Healthcare
17. Ethical and Legal Challenges
2. An Overview of Artificial Intelligence: Basics and State-of-the-Art Algorithms
3. Machine Learning for Predictive Analytics
4. Deep Learning for Biomedical Applications
5. Generative Adversarial Network for Cardiovascular Imaging
6. Natural Language Processing
7. Contemporary Advances in Medical Imaging
8. Ultrasound and Artificial Intelligence
9. Computed Tomography and Artificial Intelligence
10. Magnetic Resonance Imaging and Artificial Intelligence
11. Nuclear Imaging and Artificial Intelligence
12. Radiomics in Cardiovascular Imaging: Principles and Clinical Implications
13. Automated Interpretation of Electrocardiographic Tracings
14. Machine Learning in Cardiovascular Genomics, Proteomics, and Drug Discovery
15. Wearable Devices and Machine Learning Algorithms for Cardiovascular Health Assessment
16. The Future of Artificial Intelligence in Healthcare
17. Ethical and Legal Challenges