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Product Detail
Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls
Simon, Gyorgy J.
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List Price:$59.99
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Book Information
Edition:
1st
Publisher:
Springer Nature
ISBN:
3-031-39354-6 (3031393546)
ISBN-13:
978-3-031-39354-9 (9783031393549)
Binding:
Hardcover
Copyright:
2024
Publish Date:
03/24
Weight:
3.30 Lbs.
Pages:
810
Subject Class:
MEE (Medical Education)
Return Policy:
Returns accepted up to 12 months provided no other recalls or return restrictions apply.
Contributing Authors:
View
Class Specifications
ISSN Series:
Health Informatics
Discipline:
Medical Prof
Subject Definition:
Artificial Intelligence; Machine Learning; Medical Informat
NLM Class:
W 26.55
LC Class:
R859
Abstract:
This open access book provides a detailed review of the latest methods and applications of artificial intelligence (AI) and machine learning (ML) in medicine. With chapters focusing on enabling the reader to develop a thorough understanding of the key concepts in these subject areas along with a range of methods and resulting models that can be utilized to solve healthcare problems, the use of causal and predictive models are comprehensively discussed. Care is taken to systematically describe the concepts to facilitate the reader in developing a thorough conceptual understanding of how different methods and resulting models function and how these relate to their applicability to various issues in health care and medical sciences. Guidance is also given on how to avoid pitfalls that can be encountered on a day-to-day basis and stratify potential clinical risks. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls is a comprehensive guide to how AI and ML techniques can best be applied in health care. The emphasis placed on how to avoid a variety of pitfalls that can be encountered makes it an indispensable guide for all medical informatics professionals and physicians who utilize these methodologies on a day-to-day basis. Furthermore, this work will be of significant interest to health data scientists, administrators and to students in the health sciences seeking an up-to-date resource on the topic.
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