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Author: Kim McLeod
Publisher:
ISBN: 9781611637052
Category : Medical anthropology
Languages : en
Pages : 234
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Book Description
Wellbeing Machine shows how wellbeing arises in the intimate processes of daily life. Wellbeing and illbeing are generally seen as interior states of the individual, which can readily be linked to individuals being blamed for the status of their wellbeing. This book shifts attention away from the individual and onto the collective body. This approach generates a conceptual entity called the wellbeing machine, which comprises four assemblages that represent different responses to the challenges of everyday life experienced by people with depression. In this manner, wellbeing emerges from assemblages that transform in a sustainable way over time. Assemblages associated with illbeing are generative and vital to the production of wellbeing. Wellbeing Machine shifts discussion about the wellbeing bioeconomy into new terrain. It investigates the intersections between emergent wellbeing and labour, power, and capitalism, and produces knowledge about wellbeing that does not contribute negative associations about individuals¿ wellbeing levels.
Author: Kim McLeod
Publisher:
ISBN: 9781611637052
Category : Medical anthropology
Languages : en
Pages : 234
Get Book Here
Book Description
Wellbeing Machine shows how wellbeing arises in the intimate processes of daily life. Wellbeing and illbeing are generally seen as interior states of the individual, which can readily be linked to individuals being blamed for the status of their wellbeing. This book shifts attention away from the individual and onto the collective body. This approach generates a conceptual entity called the wellbeing machine, which comprises four assemblages that represent different responses to the challenges of everyday life experienced by people with depression. In this manner, wellbeing emerges from assemblages that transform in a sustainable way over time. Assemblages associated with illbeing are generative and vital to the production of wellbeing. Wellbeing Machine shifts discussion about the wellbeing bioeconomy into new terrain. It investigates the intersections between emergent wellbeing and labour, power, and capitalism, and produces knowledge about wellbeing that does not contribute negative associations about individuals¿ wellbeing levels.
Author: Vishal Jain
Publisher: Springer Nature
ISBN: 3030408507
Category : Technology & Engineering
Languages : en
Pages : 418
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Book Description
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.
Author: Andreas Holzinger
Publisher: Springer
ISBN: 3319504789
Category : Computers
Languages : en
Pages : 503
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Book Description
Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.
Author: Prashant Natarajan
Publisher: CRC Press
ISBN: 1315389304
Category : Medical
Languages : en
Pages : 227
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Book Description
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.
Author: Arjun Panesar
Publisher: Apress
ISBN: 1484237994
Category : Computers
Languages : en
Pages : 390
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Book Description
Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.
Author: Uli K. Chettipally
Publisher: Advantage Media Group
ISBN: 9781599329444
Category : Medical
Languages : en
Pages : 0
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Book Description
Spare The Doctor And Save The Patient The health care industry is in deep trouble. More than 50 percent of physicians report burnout and the US health care system is topping the charts for cost ?while skimming the bottom for quality among developed nations. There is a desperate need for a major shift in the health care business model and an opportunity to incorporate cutting-edge artificial intelligence (A?I) into today's health care services. In Punish the Machine! The Promise of Artificial Intelligence in Health Care, Dr. Chettipally clearly explains the current health care problems facing the US and how? ?AI technology can be used to decrease the burden on physicians, improve the quality for patients, and decrease the cost for payers.
Author: Himani Bansal
Publisher: CRC Press
ISBN: 1000406199
Category : Computers
Languages : en
Pages : 275
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Book Description
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.
Author: Guorong Wu
Publisher: Academic Press
ISBN: 0128041145
Category : Computers
Languages : en
Pages : 514
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Book Description
Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics Features self-contained chapters with a thorough literature review Assesses the development of future machine learning techniques and the further application of existing techniques
Author: Krishna Kant Singh
Publisher: Academic Press
ISBN: 012823217X
Category : Science
Languages : en
Pages : 290
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Book Description
Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide. The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks. Provides an introduction to the Internet of Medical Things through the principles and applications of machine learning Explains the functions and applications of machine learning in various applications such as ultrasound imaging, biomedical signal processing, robotics, and biomechatronics Includes coverage of the evolution of healthcare applications with machine learning, including Clinical Decision Support Systems, artificial intelligence in biomedical engineering, and AI-enabled connected health informatics, supported by real-world case studies
Author: Douglas A. Perednia
Publisher: Pearson Education
ISBN: 0132698242
Category : History
Languages : en
Pages : 16
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Book Description
This is the eBook version of the printed book. This Element is an excerpt from Overhauling America's Healthcare Machine: Stop the Bleeding and Save Trillions (9780132173254) by Douglas A. Perednia. Available in print and digital formats. Understand how the colossal U.S. healthcare machine gobbles our wealth and destroys our competitiveness--without making us healthier. Ever wonder how the world’s richest country can spend more than $2.5 trillion on healthcare annually, but still not cover more than 50 million of its citizens? Are you concerned that the huge, complex, open-ended healthcare reform law signed by President Obama didn’t solve anything and simply sets the stage for higher costs and more upheaval? If so, read on....
