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AI and Big Data in Cardiology A Practical Guide

Title
AI and Big Data in Cardiology [electronic resource] : A Practical Guide / edited by Nicolas Duchateau, Andrew P. King.
ISBN
9783031050718
Edition
1st ed. 2023.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2023.
Physical Description
1 online resource (IX, 216 p.) 56 illus., 55 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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 and informatician 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.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
July 07, 2023
Contents
Introduction
AI and Machine Learning: the Basics
From Machine Learning to Deep Learning
Measurement and Quantification
Diagnosis
Outcome Prediction
Quality Control
AI and Decision Support
AI in the Real World
Analysis of Non-imaging Data
Conclusions.
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