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Sinopsis
<DIV><DIV><P>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.</P> <P>YOU’LL DISCOVER THE ETHICAL IMPLICATIONS OF HEALTHCARE DATA ANALYTICS AND THE FUTURE OF AI IN POPULATION AND PATIENT HEALTH OPTIMIZATION.&NBSP;&NBSP;&NBSP;YOU’LL ALSO CREATE A MACHINE LEARNING MODEL, EVALUATE PERFORMANCE AND OPERATIONALIZE ITS OUTCOMES WITHIN YOUR ORGANIZATION.<I>&NBSP;</I></P> <P><I>MACHINE LEARNING AND AI FOR HEALTHCARE</I>&NBSP;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.</P></DIV><DIV><BR></DIV><DIV><B>WHAT YOU'LL LEARN</B></DIV><DIV><UL><LI>GAIN A DEEPER UNDERSTANDING OF KEY MACHINE LEARNING ALGORITHMS AND THEIR USE AND IMPLEMENTATION WITHIN WIDER HEALTHCARE&NBSP;<BR></LI><LI>IMPLEMENT MACHINE LEARNING SYSTEMS, SUCH AS SPEECH RECOGNITION AND ENHANCED DEEP LEARNING/AI<BR></LI><LI>SELECT LEARNING METHODS/ALGORITHMS AND TUNING FOR USE IN HEALTHCARE<BR></LI><LI>RECOGNIZE AND PREPARE FOR THE FUTURE OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE THROUGH BEST PRACTICES, FEEDBACK LOOPS AND INTELLIGENT AGENTS</LI></UL></DIV></DIV><DIV><B>WHO THIS BOOK IS FOR</B></DIV><DIV><DIV><BR></DIV><DIV>HEALTH CARE PROFESSIONALS&NBSP;INTERESTED IN HOW MACHINE LEARNING CAN BE USED TO DEVELOP HEALTH INTELLIGENCE – WITH THE
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Reseña del editor
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 Learn
- Gain 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 agents
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.
Biografía del autor
Arjun Panesar is the founder of Diabetes Digital Media (DDM), the world’s largest diabetes community and provider of evidence-based digital health interventions. Arjun holds a first-class honors degree (MEng) in Computing and Artificial Intelligence from Imperial College, London. Benefiting from a decade of experience in big data and affecting user outcomes, Arjun leads the development of intelligent, evidence-based digital health interventions that harness the power of big data and machine learning to provide precision patient care to patients, health agencies and governments worldwide.
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- EditorialApress
- Año de publicación2019
- ISBN 10 1484237986
- ISBN 13 9781484237984
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas396
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Machine Learning and AI for Healthcare
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