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Artificial Intelligence and Machine Learning in Health Care and Medical Sciences

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 Pitfallsis 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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Detail Information
Series Title
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Call Number
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Publisher
: Springer Nature.,
Collation
online resource
Language
English
ISBN/ISSN
9783031393556
Classification
-
Content Type
text
Media Type
computer
Carrier Type
online resource
Edition
-
Subject(s)
Predictive analytics
Specific Detail Info
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Statement of Responsibility
Other Information
Cataloger
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Source
https://directory.doabooks.org/handle/20.500.12854/135621
Validator
Suwardi
Digital Object Identifier (DOI)
10.1007/978-3-031-39355-6
Journal Volume
-
Journal Issue
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Subtitle
-
Parallel Title
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Other version/related

No other version available

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