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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.
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