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Wellbeing Machine

2017 Medical anthropology
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Release : 2017
Genre : Medical anthropology
Kind : eBook
Book Rating : 052/5 ( reviews)

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Book Synopsis Wellbeing Machine by : Kim McLeod

Download or read book Wellbeing Machine written by Kim McLeod. This book was released on 2017. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning with Health Care Perspective

2020-03-09 Technology & Engineering
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Release : 2020-03-09
Genre : Technology & Engineering
Kind : eBook
Book Rating : 507/5 ( reviews)

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Book Synopsis Machine Learning with Health Care Perspective by : Vishal Jain

Download or read book Machine Learning with Health Care Perspective written by Vishal Jain. This book was released on 2020-03-09. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Demystifying Big Data and Machine Learning for Healthcare

2017-02-15 Medical
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Release : 2017-02-15
Genre : Medical
Kind : eBook
Book Rating : 304/5 ( reviews)

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Book Synopsis Demystifying Big Data and Machine Learning for Healthcare by : Prashant Natarajan

Download or read book Demystifying Big Data and Machine Learning for Healthcare written by Prashant Natarajan. This book was released on 2017-02-15. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning and AI for Healthcare

2019-02-04 Computers
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Release : 2019-02-04
Genre : Computers
Kind : eBook
Book Rating : 994/5 ( reviews)

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Book Synopsis Machine Learning and AI for Healthcare by : Arjun Panesar

Download or read book Machine Learning and AI for Healthcare written by Arjun Panesar. This book was released on 2019-02-04. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning for Health Informatics

2016-12-09 Computers
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Release : 2016-12-09
Genre : Computers
Kind : eBook
Book Rating : 789/5 ( reviews)

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Book Synopsis Machine Learning for Health Informatics by : Andreas Holzinger

Download or read book Machine Learning for Health Informatics written by Andreas Holzinger. This book was released on 2016-12-09. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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