AI and Big Data in Cardiology : A Practical Guide

Andrew P. King

ISBN 10: 3031050738 / ISBN 13: 9783031050732

Published by Springer International Publishing, 2024

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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 applying these methodologies into day-to-day clinical practice. Chapters feature detailed technical reviews and highlight key current challenges and limitations, along with the available techniques to address them for each topic covered. Sample data sets are also included to provide hands-on tutorials for readers using Python-based Jupyter not Elektronisches Buch, and are based upon real-world examples to ensure the reader can develop their confidence in applying these techniques to solve everyday clinical problems.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.

Seller Inventory # 9783031050732

Bibliographic Details

• Title: AI and Big Data in Cardiology : A Practical Guide
• Publisher: Springer International Publishing
• Publication Date: 2024
• Language: English
• ISBN 10: 3031050738
• ISBN 13: 9783031050732
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 12D millimeters 235 millimeters width by 155 millimeters height by 12 millimeters depth
• Weight: 394 grams

About this title

Synopsis

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 applying these methodologies into day-to-day clinical practice. Chapters feature detailed technical reviews and highlight key current challenges and limitations, along with the available techniques to address them for each topic covered. Sample data sets are also included to provide hands-on tutorials for readers using Python-based Jupyter notebooks, and are based upon real-world examples to ensure the reader can develop their confidence in applying these techniques to solve everyday clinical problems.

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.

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.