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Accurate estimation, diagnosis, and prevention of COVID-19 is a
global challenge for healthcare organizations. Innovative measures
can introduce and implement AI, and Mathematical Modeling
applications. This book provides insight into the recent advances
of applications, statistical methods, and mathematical modeling for
the healthcare industry. This book covers the state-of-the-art
applications of AI and Machine Learning in past epidemics,
pandemics, and COVID-19. It offers recent global case studies, and
discusses how AI and statistical methods, initiatives, and
applications such as Machine Learning, Deep Learning, Correlation
and Regression Analysis play a major role in the prediction,
diagnosis, and prevention of a pandemic. It will also focus on how
AI and statistical applications can facilitate and restructure the
healthcare system. This book is written for Researchers, Students,
Professionals, Executives, and the general public.
Covers applications of AI & IoT in healthcare Discusses recent
case studies Focuses on how applications can facilitate and
structure the healthcare systems Promotes scholarship and research
Provides how applications can unleash the potential to keep
patients safe and health, and also empower physicians to deliver
better care
This book focuses on contemporary technologies and research in
computational intelligence that has reached the practical level and
is now accessible in preclinical and clinical settings. This book's
principal objective is to thoroughly understand significant
technological breakthroughs and research results in predictive
modeling in healthcare imaging and data analysis. Machine learning
and deep learning could be used to fully automate the diagnosis and
prognosis of patients in medical fields. The healthcare industry's
emphasis has evolved from a clinical-centric to a patient-centric
model. However, it is still facing several technical,
computational, and ethical challenges. Big data analytics in health
care is becoming a revolution in technical as well as societal
well-being viewpoints. Moreover, in this age of big data,
there is increased access to massive amounts of regularly gathered
data from the healthcare industry that has necessitated the
development of predictive models and automated solutions for the
early identification of critical and chronic illnesses. The book
contains high-quality, original work that will assist readers in
realizing novel applications and contexts for deep learning
architectures and algorithms, making it an indispensable reference
guide for academic researchers, professionals, industrial software
engineers, and innovative model developers in healthcare industry.
This book focuses on contemporary technologies and research in
computational intelligence that has reached the practical level and
is now accessible in preclinical and clinical settings. This book's
principal objective is to thoroughly understand significant
technological breakthroughs and research results in predictive
modeling in healthcare imaging and data analysis. Machine learning
and deep learning could be used to fully automate the diagnosis and
prognosis of patients in medical fields. The healthcare industry's
emphasis has evolved from a clinical-centric to a patient-centric
model. However, it is still facing several technical,
computational, and ethical challenges. Big data analytics in health
care is becoming a revolution in technical as well as societal
well-being viewpoints. Moreover, in this age of big data, there is
increased access to massive amounts of regularly gathered data from
the healthcare industry that has necessitated the development of
predictive models and automated solutions for the early
identification of critical and chronic illnesses. The book contains
high-quality, original work that will assist readers in realizing
novel applications and contexts for deep learning architectures and
algorithms, making it an indispensable reference guide for academic
researchers, professionals, industrial software engineers, and
innovative model developers in healthcare industry.
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