Machine Learning in Cardiovascular Medicine

Machine Learning in Cardiovascular Medicine addresses the ever-expanding applications of artificial intelligence (AI), and specifically machine learning (ML), in health care and within cardiovascular medicine. AI was initially born out of the need to solve non-linear and combinatorially complex tasks. Over time, AI has transformed innumerable aspects of human life; the development of augmented reality, autonomous driving, robotics as well as sophisticated predictive modelling are just a few examples of how AI has sparked the current innovative climate within the industry sector. While the industrial applications of ML are nearly ubiquitous, its introduction into the medical field has been much more gradual. The landscape, however, is rapidly changing as a result of the availability of computational power as well as the creation of large repositories of datasets. The computer science and medical communities are increasingly streamlining efforts at harnessing the potential of ML in order to solve complex tasks and optimize day-to-day workflow in the healthcare sector. This book is focused on emphasizing ML for biomedical applications and aims at providing a comprehensive summary of the past and present of AI, basics of ML as well as clinical applications of ML within cardiovascular medicine for predictive analytics and precision medicine. Provide an overview of machine learning both to a clinical and engineering audience narrated by experts in the fieldSummarize recent advances in both cardiovascular medicine and artificial intelligence that enabled the application of machine learning in cardiovascular researchDiscuss the advantages of using machine learning for outcomes research and image processing, as pertaining to the various cardiovascular imaging modalitiesAddress the ever-expanding application of this novel technology and discuss some of the unique challenges associated with such an approachProvide a template for clinicians to understand areas of application of machine learning within cardiovascular researchAssist computer scientists and engineers in evaluating current and future impact of machine learning on cardiovascular medicine INDICE: 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. Generalized Adversarial Networks (GANs) 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. Multimodal Imaging Application of Artificial Intelligence 13. Radiomics and Precision Medicine 14. Automated Interpretation of Electrocardiographic Tracings 15. Application of Machine Learning to Genomics, Proteomics, and Cardiovascular Drug Discovery 16. Wearable Devices and Machine Learning Algorithms for Health Assessment 17. Future of Artificial Intelligence and Healthcare 18. Ethical and Legal Challenges

• ISBN: 978-0-12-820273-9
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 365
• Fecha Publicación: 01/08/2020
• Nº Volúmenes: 1
• Idioma: Inglés