AI and Big Data in Cardiology: A Practical Guide

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 have significant potential to improve clinical practice. Chapters feature technical reviews, highlight challenges, and provide techniques to address them. Sample data sets are included for hands-on tutorials using Python-based Jupyter notebooks.

Format: Hardback
Length: 216 pages
Publication date: 05 February 2023
Publisher: Springer International Publishing AG

The book is organized into five distinct chapters, each of which focuses on a specific aspect of AI and big data in cardiology. The first chapter provides an introduction to the field, including an overview of the different types of AI and machine learning algorithms used in cardiology, as well as their applications and limitations. The second chapter explores the use of AI and machine learning in the diagnosis and treatment of cardiovascular diseases, including heart failure, arrhythmias, and coronary artery disease. The third chapter focuses on the use of big data in cardiology, including data mining, data analysis, and machine learning techniques for analyzing large datasets and extracting valuable insights. The fourth chapter discusses the use of AI and big data in cardiovascular imaging, including CT scans, MRI scans, and echocardiography. The fifth chapter explores the use of AI and big data in cardiovascular research, including the development of new algorithms, models, and tools for analyzing data and predicting outcomes.

Recent technological advancements in artificial intelligence (AI), machine learning, and big data have opened up significant opportunities for their application in cardiology. These fields have the potential to revolutionize the way we diagnose, treat, and manage cardiovascular diseases. In this book, we will provide a comprehensive technical overview of the use and applications of AI, machine learning, and big data in cardiology.

Artificial intelligence (AI) is a branch of computer science that focuses on the development of algorithms that can perform tasks that typically require human intelligence. AI has been applied in cardiology in various ways, such as image analysis, diagnosis, and treatment planning. For example, AI algorithms can be used to analyze medical images, such as CT scans and MRI scans, to detect heart disease and other cardiovascular conditions. AI can also be used to create personalized treatment plans for patients based on their medical history, symptoms, and test results.

Machine learning is a subset of AI that involves the use of algorithms to learn from data and make predictions. Machine learning algorithms can be used to analyze large datasets, such as patient records and clinical trials, to identify patterns and trends that can help us better understand and manage cardiovascular diseases. Machine learning algorithms can also be used to develop predictive models that can help us predict the risk of developing cardiovascular diseases, the likelihood of a patient responding to a particular treatment, and the likelihood of a patient experiencing a cardiovascular event.

Big data is a term used to describe the large volumes of data that are generated by various sources, such as medical devices, social media, and clinical trials. Big data has the potential to transform the way we approach healthcare by providing us with new insights and opportunities to improve patient care. In cardiology, big data can be used to analyze patient records, clinical trials, and other datasets to identify patterns and trends that can help us better understand and manage cardiovascular diseases. Big data can also be used to develop new algorithms and models that can help us predict the risk of developing cardiovascular diseases, the likelihood of a patient responding to a particular treatment, and the likelihood of a patient experiencing a cardiovascular event.

In this book, we will explore the latest advancements in AI, machine learning, and big data in cardiology. We will discuss the different types of AI and machine learning algorithms used in cardiology, as well as their applications and limitations. We will also discuss the use of big data in cardiology, including data mining, data analysis, and machine learning techniques for analyzing large datasets and extracting valuable insights.

We will also provide hands-on tutorials for readers using Python-based Jupyter notebooks, which will allow them to develop their confidence in applying these techniques to solve everyday clinical problems. The tutorials will be based on real-world examples to ensure that readers can develop their confidence in applying these techniques to solve everyday clinical problems.

In conclusion, this book provides a comprehensive technical overview of the use and applications of AI, machine learning, and big data in 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.

Weight: 549g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031050701
Edition number: 1st ed. 2023