Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics

Demystifying Big Data  Machine Learning  and Deep Learning for Healthcare Analytics
Author: Pradeep N,Sandeep Kautish,Sheng-Lung Peng
Publsiher: Academic Press
Total Pages: 372
Release: 2021-06-25
ISBN 10: 0128220449
ISBN 13: 9780128220443
Language: EN, FR, DE, ES & NL

Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics Book Review:

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians. Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables, and graphs with algorithms and computational methods for developing new applications in healthcare informatics Unique case study approach provides readers with insights for practical clinical implementation

Demystifying Big Data and Machine Learning for Healthcare

Demystifying Big Data and Machine Learning for Healthcare
Author: Prashant Natarajan,John C. Frenzel,Detlev H. Smaltz
Publsiher: CRC Press
Total Pages: 210
Release: 2017-02-15
ISBN 10: 1315389312
ISBN 13: 9781315389318
Language: EN, FR, DE, ES & NL

Demystifying Big Data and Machine Learning for Healthcare Book Review:

Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.

Demystifying Big Data and Machine Learning for Healthcare

Demystifying Big Data and Machine Learning for Healthcare
Author: Prashant Natarajan,John C. Frenzel,Detlev H. Smaltz
Publsiher: CRC Press
Total Pages: 210
Release: 2017-02-15
ISBN 10: 1315389304
ISBN 13: 9781315389301
Language: EN, FR, DE, ES & NL

Demystifying Big Data and Machine Learning for Healthcare Book Review:

Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.

Demystifying AI for the Enterprise

Demystifying AI for the Enterprise
Author: Prashant Natarajan,Bob Rogers,Edward Dixon,Jonas Christensen,Kirk Borne,Leland Wilkinson,Shantha Mohan
Publsiher: CRC Press
Total Pages: 434
Release: 2021-12-31
ISBN 10: 1351032925
ISBN 13: 9781351032926
Language: EN, FR, DE, ES & NL

Demystifying AI for the Enterprise Book Review:

Artificial intelligence (AI) in its various forms –– machine learning, chatbots, robots, agents, etc. –– is increasingly being seen as a core component of enterprise business workflow and information management systems. The current promise and hype around AI are being driven by software vendors, academic research projects, and startups. However, we posit that the greatest promise and potential for AI lies in the enterprise with its applications touching all organizational facets. With increasing business process and workflow maturity, coupled with recent trends in cloud computing, datafication, IoT, cybersecurity, and advanced analytics, there is an understanding that the challenges of tomorrow cannot be solely addressed by today’s people, processes, and products. There is still considerable mystery, hype, and fear about AI in today’s world. A considerable amount of current discourse focuses on a dystopian future that could adversely affect humanity. Such opinions, with understandable fear of the unknown, don’t consider the history of human innovation, the current state of business and technology, or the primarily augmentative nature of tomorrow’s AI. This book demystifies AI for the enterprise. It takes readers from the basics (definitions, state-of-the-art, etc.) to a multi-industry journey, and concludes with expert advice on everything an organization must do to succeed. Along the way, we debunk myths, provide practical pointers, and include best practices with applicable vignettes. AI brings to enterprise the capabilities that promise new ways by which professionals can address both mundane and interesting challenges more efficiently, effectively, and collaboratively (with humans). The opportunity for tomorrow’s enterprise is to augment existing teams and resources with the power of AI in order to gain competitive advantage, discover new business models, establish or optimize new revenues, and achieve better customer and user satisfaction.

Digital Health Transformation with Blockchain and Artificial Intelligence

Digital Health Transformation with Blockchain and Artificial Intelligence
Author: Chinmay Chakraborty
Publsiher: CRC Press
Total Pages: 368
Release: 2022-05-11
ISBN 10: 1000580946
ISBN 13: 9781000580945
Language: EN, FR, DE, ES & NL

Digital Health Transformation with Blockchain and Artificial Intelligence Book Review:

The book Digital Health Transformation with Blockchain and Artificial Intelligence covers the global digital revolution in the field of healthcare sector. The population has been overcoming the COVID-19 period; therefore, we need to establish intelligent digital healthcare systems using various emerging technologies like Blockchain and Artificial Intelligence. Internet of Medical Things is the technological revolution that has included the element of "smartness" in the healthcare industry and also identifying, monitoring, and informing service providers about the patient’s clinical information with faster delivery of care services. This book highlights the important issues i.e. (a) How Internet of things can be integrated with the healthcare ecosystem for better diagnostics, monitoring, and treatment of the patients, (b) Artificial Intelligence for predictive and preventive healthcare systems, (c) Blockchain for managing healthcare data to provide transparency, security, and distributed storage, and (d) Effective remote diagnostics and telemedicine approach for developing smart care. The book encompasses chapters belong to the blockchain, Artificial Intelligence, and Big health data technologies. Features: Blockchain and internet of things in healthcare systems Secure Digital Health Data Management in Internet of Things Public Perception towards AI-Driven Healthcare Security, privacy issues and challenges in adoption of smart digital healthcare Big data analytics and Internet of things in the pandemic era Clinical challenges for digital health revolution Artificial intelligence for advanced healthcare Future Trajectory of Healthcare with Artificial Intelligence 9 Parkinson disease pre-diagnosis using smart technologies Emerging technologies to combat the COVID-19 Machine Learning and Internet of Things in Digital Health Transformation Effective Remote Healthcare and Telemedicine Approaches Legal implication of blockchain technology in public health This Book on "Digital Health Transformation with Blockchain and Artificial Intelligence" aims at promoting and facilitating exchanges of research knowledge and findings across different disciplines on the design and investigation of secured healthcare data analytics. It can also be used as a textbook for a Masters course in security and biomedical engineering. This book will also present new methods for the medical data analytics, blockchain technology, and diagnosis of different diseases to improve the quality of life in general, and better integration into digital healthcare.

Emerging Technologies in Healthcare

Emerging Technologies in Healthcare
Author: Matthew N. O. Sadiku,Rotimi A. K. Jaiyesimi,Joyce B. Idehen,Sarhan M. Musa
Publsiher: AuthorHouse
Total Pages: 316
Release: 2021-10-05
ISBN 10: 1665528427
ISBN 13: 9781665528429
Language: EN, FR, DE, ES & NL

Emerging Technologies in Healthcare Book Review:

Health is regarded as one of the global challenges for mankind. Healthcare is a complex system that covers processes of diagnosis, treatment, and prevention of diseases. It constitutes a fundamental pillar of the modern society. Modern healthcare is technological healthcare. Technology is everywhere. This book focuses on twenty-one emerging technologies in the healthcare industry. An emerging technology is one that holds the promise of creating a new economic engine and is trans-industrial. Emerging technological trends are rapidly transforming businesses in general and healthcare in particular in ways that we find hard to imagine. Artificial intelligence (AI), machine learning, robots, blockchain, cloud computing, Internet of things (IoT), and augmented & virtual reality are some of the technologies at the heart of this revolution and are covered in this book. The convergence of these technologies is upon us and will have a huge impact on the patient experience

Big Data and Artificial Intelligence for Healthcare Applications

Big Data and Artificial Intelligence for Healthcare Applications
Author: Ankur Saxena,Nicolas Brault,Shazia Rashid
Publsiher: CRC Press
Total Pages: 286
Release: 2021-06-15
ISBN 10: 1000387313
ISBN 13: 9781000387315
Language: EN, FR, DE, ES & NL

Big Data and Artificial Intelligence for Healthcare Applications Book Review:

This book covers a wide range of topics on the role of Artificial Intelligence, Machine Learning, and Big Data for healthcare applications and deals with the ethical issues and concerns associated with it. This book explores the applications in different areas of healthcare and highlights the current research. "Big Data and Artificial Intelligence for Healthcare Applications" covers healthcare big data analytics, mobile health and personalized medicine, clinical trial data management and presents how Artificial Intelligence can be used for early disease diagnosis prediction and prognosis. It also offers some case studies that describes the application of Artificial Intelligence and Machine Learning in healthcare. Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics

Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics
Author: Abhishek Kumar,Anavatti G. Sreenatha,Ashutosh Kumar Dubey,Pramod Singh Rathore
Publsiher: CRC Press
Total Pages: 224
Release: 2022
ISBN 10: 9781003132110
ISBN 13: 1003132111
Language: EN, FR, DE, ES & NL

Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics Book Review:

"In the last two decades, machine learning has been dramatically developed and is still experiencing a fast and ever-lasting change in paradigm, methodology, applications, and other aspects. This book offers a compendium of current and emerging machine learning paradigms in healthcare informatics and reflects on the diversity and complexity. Machine Learning Approaches and Applications Applied Intelligence for Healthcare Data Analytics presents a variety of techniques design to enhance and empower multi-disciplinary and multi-institutional machine learning research. It provides many case studies and a panoramic view of data and machine learning techniques providing the opportunity for novel insights and discoveries. The book explores the theory and practical applications in healthcare and includes a guided tour of machine learning algorithms, architecture design, along with interdisciplinary challenges. This book is useful to research scholars and students involved in critical condition analysis and computation models"--

Exploratory Data Analytics for Healthcare

Exploratory Data Analytics for Healthcare
Author: R. Lakshmana Kumar,R. Indrakumari,B. Balamurugan,Achyut Shankar
Publsiher: CRC Press
Total Pages: 312
Release: 2021-12-24
ISBN 10: 1000527018
ISBN 13: 9781000527018
Language: EN, FR, DE, ES & NL

Exploratory Data Analytics for Healthcare Book Review:

Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today. The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain.

Machine Learning and Analytics in Healthcare Systems

Machine Learning and Analytics in Healthcare Systems
Author: Himani Bansal,Balamurugan Balusamy,T. Poongodi,Firoz Khan KP
Publsiher: CRC Press
Total Pages: 274
Release: 2021-07-01
ISBN 10: 1000406199
ISBN 13: 9781000406191
Language: EN, FR, DE, ES & NL

Machine Learning and Analytics in Healthcare Systems Book Review:

This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine. It will combine the design and problem-solving skills of engineering with health sciences, in order to advance healthcare treatment. The book will include areas such as diagnosis, monitoring, and therapy. The book will provide real-world case studies, gives a detailed exploration of applications in healthcare systems, offers multiple perspectives on a variety of disciplines, while also letting the reader know how to avoid some of the consequences of old methods with data sharing. The book can be used as a reference for practitioners, researchers and for students at basic and intermediary levels in Computer Science, Electronics and Communications.

Handbook of Research on Emerging Trends and Applications of Machine Learning

Handbook of Research on Emerging Trends and Applications of Machine Learning
Author: Solanki, Arun,Kumar, Sandeep,Nayyar, Anand
Publsiher: IGI Global
Total Pages: 674
Release: 2019-12-13
ISBN 10: 1522596453
ISBN 13: 9781522596455
Language: EN, FR, DE, ES & NL

Handbook of Research on Emerging Trends and Applications of Machine Learning Book Review:

As today’s world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning.

Artificial Intelligence in Healthcare

Artificial Intelligence in Healthcare
Author: Adam Bohr,Kaveh Memarzadeh
Publsiher: Academic Press
Total Pages: 378
Release: 2020-06-21
ISBN 10: 0128184396
ISBN 13: 9780128184394
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Healthcare Book Review:

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data

A Global Approach to Data Value Maximization

A Global Approach to Data Value Maximization
Author: Paolo Dell’Aversana
Publsiher: Cambridge Scholars Publishing
Total Pages: 226
Release: 2019-04-17
ISBN 10: 1527533379
ISBN 13: 9781527533370
Language: EN, FR, DE, ES & NL

A Global Approach to Data Value Maximization Book Review:

This book presents a systematic discussion about methods and techniques used to extract the maximum informative value from complex data sets. A multitude of approaches and techniques can be applied for that purpose, including data fusion and model integration, multimodal data analysis in different physical domains, audio-video display of data through techniques of “sonification”, multimedia machine learning, and hybrid methods of data analysis. The book begins with the domain of geosciences, before moving on to other scientific areas, like diagnostic medicine and various engineering sectors. As such, it will appeal to a large audience, including geologists and geophysicists, data scientists, physicians and cognitive scientists, and experts in social sciences and knowledge management.

Machine Learning with Health Care Perspective

Machine Learning with Health Care Perspective
Author: Vishal Jain,Jyotir Moy Chatterjee
Publsiher: Springer Nature
Total Pages: 415
Release: 2020-03-09
ISBN 10: 3030408507
ISBN 13: 9783030408503
Language: EN, FR, DE, ES & NL

Machine Learning with Health Care Perspective Book Review:

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.

Machine Learning for Healthcare

Machine Learning for Healthcare
Author: Rashmi Agrawal,Jyotir Moy Chatterjee,Abhishek Kumar,Pramod Singh Rathore,Dac-Nhuong Le
Publsiher: CRC Press
Total Pages: 204
Release: 2020-12-08
ISBN 10: 1000221784
ISBN 13: 9781000221787
Language: EN, FR, DE, ES & NL

Machine Learning for Healthcare Book Review:

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector. The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors.

Artificial Intelligence and Internet of Things

Artificial Intelligence and Internet of Things
Author: Lalit Mohan Goyal,Tanzila Saba,Amjad Rehman,Souad Larabi-Marie-Sainte
Publsiher: CRC Press
Total Pages: 398
Release: 2021-08-25
ISBN 10: 1000386252
ISBN 13: 9781000386257
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Internet of Things Book Review:

This book reveals the applications of AI and IoT in smart healthcare and medical systems. It provides core principles, algorithms, protocols, emerging trends, security problems, and the latest e-healthcare services findings. The book also provides case studies and discusses how AI and IoT applications such as wireless devices, sensors, and deep learning could play a major role in assisting patients, doctors, and pharmaceutical staff. It focuses on how to use AI and IoT to keep patients safe and healthy and, at the same time, empower physicians to deliver superlative care. This book is written for researchers and practitioners working in the information technology, computer science, and medical equipment manufacturing industry for products and services having basic- and high-level AI and IoT applications. The book is also a useful guide for academic researchers and students.

Advances in Surgery 2020

Advances in Surgery 2020
Author: John L. Cameron
Publsiher: Elsevier Health Sciences
Total Pages: 240
Release: 2020-08-30
ISBN 10: 0323755240
ISBN 13: 9780323755245
Language: EN, FR, DE, ES & NL

Advances in Surgery 2020 Book Review:

Each year, Advances in Surgery reviews the most current practices in general surgery. A distinguished editorial board, headed by Dr. John Cameron, identifies key areas of major progress and controversy and invites preeminent specialists to contribute original articles devoted to these topics. These insightful overviews in general surgery bring concepts to a clinical level and explore their everyday impact on patient care.

Research Anthology on Telemedicine Efficacy Adoption and Impact on Healthcare Delivery

Research Anthology on Telemedicine Efficacy  Adoption  and Impact on Healthcare Delivery
Author: Management Association, Information Resources
Publsiher: IGI Global
Total Pages: 689
Release: 2021-01-15
ISBN 10: 1799881075
ISBN 13: 9781799881070
Language: EN, FR, DE, ES & NL

Research Anthology on Telemedicine Efficacy Adoption and Impact on Healthcare Delivery Book Review:

Telemedicine, which involves electronic communications and software, provides the same clinical services to patients without the requirement of an in-person visit. Essentially, this is considered remote healthcare. Though telemedicine is not a new practice, it has become an increasingly popular form of healthcare delivery due to current events, including the COVID-19 pandemic. Not only are visits being moved onto virtual platforms, but additional materials and correspondence can remain in the digital sphere. Virtual lab results, digital imaging, medical diagnosis, and video consultations are just a few examples that encompass how telemedicine can be used for increased accessibility in healthcare delivery. With telemedicine being used in both the diagnosis and treatment of patients, technology in healthcare can be implemented at almost any phase of the patient experience. As healthcare delivery follows the digital shift, it is important to understand the technologies, benefits and challenges, and overall impacts of the remote healthcare experience. The Research Anthology on Telemedicine Efficacy, Adoption, and Impact on Healthcare Delivery presents the latest research on best practices for adopting telehealth into medical practices and its efficacy and solutions for the improvement of telemedicine, as well as addresses emerging challenges and opportunities, including issues such as securing patient data and providing healthcare accessibility to rural populations. Covering important themes that include doctor-patient relationships, tele-wound monitoring, and telemedicine regulations, this book is essential for healthcare professionals, doctors, medical students, academic and medical libraries, medical technologists, practitioners, stakeholders, researchers, academicians, and students interested in the emerging technological developments and solutions within the field of telemedicine.

Impacts of Information Technology on Patient Care and Empowerment

Impacts of Information Technology on Patient Care and Empowerment
Author: McHaney, Roger W.,Reychev, Iris,Azuri, Joseph,McHaney, Mark E.,Moshonov, Rami
Publsiher: IGI Global
Total Pages: 452
Release: 2019-09-20
ISBN 10: 1799800482
ISBN 13: 9781799800484
Language: EN, FR, DE, ES & NL

Impacts of Information Technology on Patient Care and Empowerment Book Review:

Modern technology has impacted healthcare and interactions between patients and healthcare providers through a variety of means including the internet, social media, mobile devices, and the internet of things. These new technologies have empowered, frustrated, educated, and confused patients by making educational materials more widely available and allowing patients to monitor their own vital signs and self-diagnose. Further analysis of these and future technologies is needed in order to provide new approaches to empowerment, reduce mistakes, and improve overall healthcare. Impacts of Information Technology on Patient Care and Empowerment is a critical scholarly resource that delves into patient access to information and the effect that access has on their relationship with healthcare providers and their health outcomes. Featuring a range of topics such as gamification, mobile computing, and risk analysis, this book is ideal for healthcare practitioners, doctors, nurses, surgeons, hospital staff, medical administrators, patient advocates, researchers, academicians, policymakers, and healthcare students.

Artificial Intelligence and Big Data Analytics for Smart Healthcare

Artificial Intelligence and Big Data Analytics for Smart Healthcare
Author: Miltiadis Lytras,Akila Sarirete,Anna Visvizi,Kwok Tai Chui
Publsiher: Academic Press
Total Pages: 290
Release: 2021-10-22
ISBN 10: 0128220627
ISBN 13: 9780128220627
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Big Data Analytics for Smart Healthcare Book Review:

Artificial Intelligence and Big Data Analytics for Smart Healthcare serves as a key reference for practitioners and experts involved in healthcare as they strive to enhance the value added of healthcare and develop more sustainable healthcare systems. It brings together insights from emerging sophisticated information and communication technologies such as big data analytics, artificial intelligence, machine learning, data science, medical intelligence, and, by dwelling on their current and prospective applications, highlights managerial and policymaking challenges they may generate. The book is split into five sections: big data infrastructure, framework and design for smart healthcare; signal processing techniques for smart healthcare applications; business analytics (descriptive, diagnostic, predictive and prescriptive) for smart healthcare; emerging tools and techniques for smart healthcare; and challenges (security, privacy, and policy) in big data for smart healthcare. The content is carefully developed to be understandable to different members of healthcare chain to leverage collaborations with researchers and industry. Presents a holistic discussion on the new landscape of data driven medical technologies including Big Data, Analytics, Artificial Intelligence, Machine Learning, and Precision Medicine Discusses such technologies with case study driven approach with reference to real world application and systems, to make easier the understanding to the reader not familiar with them Encompasses an international collaboration perspective, providing understandable knowledge to professionals involved with healthcare to leverage productive partnerships with technology developers