AI and Big Data in Cardiology
Language: English
Published by Springer, Berlin|Springer International Publishing|Springer, 2024
- Softcover
- New
Condition: New
£ 44.98
Quantity: Over 20 available
Add to basketItem description from seller
Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book provides a detailed technical overview of the use and applications of artificial intelligence (AI), machine learning and big data in cardiology. Recent technological advancements in these fields mean that there is significant gain to be had in app.
Seller Inventory # 1612335557
- Title
- AI and Big Data in Cardiology
- Publisher
- Springer, Berlin|Springer International Publishing|Springer
- Publication year
- 2024
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 3031050738
- ISBN 13
- 9783031050732
Artificial Intelligence and Big Data in Cardiology systematically describes and technically reviews the latest applications of AI and big data within cardiology. It is ideal for use by the trainee and practicing cardiologist andinformatician seeking an up-to-date resource on the topic with which to aid them in developing a thorough understanding of both basic concepts and recent advances in the field.
"Synopsis" may belong to another edition of this title.
About the Author
Dr. Duchateau is currently Associate Professor (Maître de Conférences) at the Université Lyon 1 and the CREATIS lab in Lyon, France, and Junior Member of the Institut Universitaire de France (IUF). His research is on the development of new statistical and machine learning approaches for the better understanding of disease apparition and evolution from medical imaging data. On the applicative side, his work has special dedication to the study of cardiac function and 2D/3D myocardial shape, motion and deformation patterns. It has a strong focus on heart failure populations, looked through echocardiographic and magnetic resonance imaging data.
Dr. King is currently a Reader in Medical Image Analysis at the School of Biomedical Engineering and Imaging Sciences at King’s College London. His research aims to develop novel machine learning methods for a range of medical applications, but with a special focus on cardiology. He works closely with clinicians to exploit the power of machine learning to solve clinically relevant problems. Notable recent successes include the prediction of treatment outcome for heart failure and automated quantification of cardiac function for patient risk stratification."About the title" may belong to another edition of this title.
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