Description
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
Table of Contents
1. Technological Advances within Digital Medicine2. An Overview of Artificial Intelligence: Basics and State-of-the-Art Algorithms3. Machine Learning for Predictive Analytics4. Deep Learning for Biomedical Applications5. Generative Adversarial Network for Cardiovascular Imaging6. Natural Language Processing7. Contemporary Advances in Medical Imaging8. Ultrasound and Artificial Intelligence9. Computed Tomography and Artificial Intelligence10. Magnetic Resonance Imaging and Artificial Intelligence11. Nuclear Imaging and Artificial Intelligence12. Radiomics in Cardiovascular Imaging: Principles and Clinical Implications13. Automated Interpretation of Electrocardiographic Tracings14. Machine Learning in Cardiovascular Genomics, Proteomics, and Drug Discovery15. Wearable Devices and Machine Learning Algorithms for Cardiovascular Health Assessment16. The Future of Artificial Intelligence in Healthcare17. Ethical and Legal Challenges
