Predicting Heart Failure

Invasive, Non-Invasive, Machine Learning, and Artificial Intelligence Based Methods

Kishor Kumar Sadasivuni, Hassen M. Ouakad, Somaya Al-Maadeed, Huseyin C. Yalcin, Issam Bait Bahadur

• Publisher: Wiley
• Year: 2022
• ISBN:
  9781119813026
• Edition: 1
• Topic: Medicine
• Subtopic: Cardiology

PREDICTING HEART FAILURE

Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods focuses on the mechanics and symptoms of heart failure and various approaches, including conventional and modern techniques to diagnose it.

This book also provides a comprehensive but concise guide to all modern cardiological practice, emphasizing practical clinical management in many different contexts. Predicting Heart Failure supplies readers with trustworthy insights into all aspects of heart failure, including essential background information on clinical practice guidelines, in-depth, peer-reviewed articles, and broad coverage of this fast-moving field. Readers will also find:

• Discussion of the main characteristics of cardiovascular biosensors, along with their open issues for development and application
• Summary of the difficulties of wireless sensor communication and power transfer, and the utility of artificial intelligence in cardiology
• Coverage of data mining classification techniques, applied machine learning and advanced methods for estimating HF severity and diagnosing and predicting heart failure
• Discussion of the risks and issues associated with the remote monitoring system
• Assessment of the potential applications and future of implantable and wearable devices in heart failure prediction and detection
• Artificial intelligence in mobile monitoring technologies to provide clinicians with improved treatment options, ultimately easing access to healthcare by all patient populations.

Providing the latest research data for the diagnosis and treatment of heart failure, Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods is an excellent resource for nurses, nurse practitioners, physician assistants, medical students, and general practitioners to gain a better understanding of bedside cardiology.

Table of contents

• Predicting Heart Failure
• Contents
• Preface
• Abbreviations
• Acknowledgment
• 1 Invasive, Non-Invasive, Machine Learning, and Artificial Intelligence Based Methods for Prediction of Heart Failure
• 2 Conventional Clinical Methods for Predicting Heart Disease
• 3 Types of Biosensors and their Importance in Cardiovascular Applications
• 4 Overview and Challenges of Wireless Communication and Power Transfer for Implanted Sensors
• 5 Minimally Invasive and Non-Invasive Sensor Technologies for Predicting Heart Failure: An Overview
• 6 Artificial Intelligence Techniques in Cardiology: An Overview
• 7 Utilizing Data Mining Classification Algorithms for Early Diagnosis of Heart Diseases
• 8 Applications of Machine Learning for Predicting Heart Failure
• 9 Machine Learning Techniques for Predicting and Managing Heart Failure
• 10 Clinical Applications of Artificial Intelligence in Early and Accurate Detection of Low- Concentration CVD Biomarkers
• 11 Commercial Non-Invasive and Invasive Devices for Heart Failure Prediction: A Review
• 12 Artificial Intelligence Based Commercial Non-Invasive and Invasive Devices for Heart Failure Diagnosis and Prediction
• 13 Future Techniques and Perspectives on Implanted and Wearable Heart Failure Detection Devices
• Index
• EULA