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.
1. Introduction - p. xxii 2. Chapter 1. Technological advances within digital medicine - p. 1 3. Chapter 2. An overview of artificial intelligence: basics and state-of-the-art algorithms - p. 27 4. Chapter 3. Machine learning for predictive analytics - p. 45 5. Chapter 4. Deep learning for biomedical applications - p. 71 6. Chapter 5. Generative adversarial network for cardiovascular imaging - p. 95 7. Chapter 6. Natural language processing - p. 123 8. Chapter 7. Contemporary advances in medical imaging - p. 149 9. Chapter 8. Ultrasound and artificial intelligence - p. 177 10. Chapter 9. Computed tomography and artificial intelligence - p. 211 11. Chapter 10. Magnetic resonance imaging and artificial intelligence - p. 241 12. Chapter 11. Nuclear imaging and artificial intelligence - p. 255 13. Chapter 12. Radiomics in cardiovascular imaging: principles and clinical implications - p. 281 14. Chapter 13. Automated interpretation of electrocardiographic tracings - p. 311 15. Chapter 14. Machine learning in cardiovascular genomics, proteomics, and drug discovery - p. 325 16. Chapter 15. Wearable devices and machine learning algorithms for cardiovascular health assessment - p. 353 17. Chapter 16. The future of artificial intelligence in healthcare - p. 371 18. Chapter 17. Ethical and legal challenges - p. 395 19. Glossary - p. 411 20. Index - p. 420
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.
1. Introduction - p. xxii 2. Chapter 1. Technological advances within digital medicine - p. 1 3. Chapter 2. An overview of artificial intelligence: basics and state-of-the-art algorithms - p. 27 4. Chapter 3. Machine learning for predictive analytics - p. 45 5. Chapter 4. Deep learning for biomedical applications - p. 71 6. Chapter 5. Generative adversarial network for cardiovascular imaging - p. 95 7. Chapter 6. Natural language processing - p. 123 8. Chapter 7. Contemporary advances in medical imaging - p. 149 9. Chapter 8. Ultrasound and artificial intelligence - p. 177 10. Chapter 9. Computed tomography and artificial intelligence - p. 211 11. Chapter 10. Magnetic resonance imaging and artificial intelligence - p. 241 12. Chapter 11. Nuclear imaging and artificial intelligence - p. 255 13. Chapter 12. Radiomics in cardiovascular imaging: principles and clinical implications - p. 281 14. Chapter 13. Automated interpretation of electrocardiographic tracings - p. 311 15. Chapter 14. Machine learning in cardiovascular genomics, proteomics, and drug discovery - p. 325 16. Chapter 15. Wearable devices and machine learning algorithms for cardiovascular health assessment - p. 353 17. Chapter 16. The future of artificial intelligence in healthcare - p. 371 18. Chapter 17. Ethical and legal challenges - p. 395 19. Glossary - p. 411 20. Index - p. 420
