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  • 1
    UID:
    edocfu_BV049640335
    Format: 1 Online-Ressource.
    ISBN: 978-3-031-39355-6
    Series Statement: Health informatics
    Additional Edition: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-031-39354-9
    Additional Edition: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-39357-0
    Language: English
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    edoccha_BV049640335
    Format: 1 Online-Ressource.
    ISBN: 978-3-031-39355-6
    Series Statement: Health informatics
    Additional Edition: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-031-39354-9
    Additional Edition: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-39357-0
    Language: English
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 3
    UID:
    b3kat_BV049640335
    Format: 1 Online-Ressource
    ISBN: 9783031393556
    Series Statement: Health informatics
    Additional Edition: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-031-39354-9
    Additional Edition: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-39357-0
    Language: English
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
    Library Location Call Number Volume/Issue/Year Availability
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  • 4
    UID:
    almahu_9949705930002882
    Format: 1 online resource (XXVI, 810 p. 146 illus., 130 illus. in color.)
    Edition: 1st ed. 2024.
    ISBN: 3-031-39355-4
    Series Statement: Health Informatics,
    Content: 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.
    Note: Predictive Analytics -- Machine Learning -- Artificial Intelligence -- Data Mining -- Clinical Risk Models -- Clinical Risk Stratification -- Data Science -- Causal Discovery -- Causal Inference -- Causal Discovery in Health Sciences -- Causal Inference In Health Sciences -- Ehr Data Analytics -- Medical Knowledge Discovery -- Biomedical Machine Learning -- Biomedical Artificial Intelligence -- Healthcare Machine Learning -- Healthcare Artificial Intelligence -- Translational Science Machine Learning -- Machine Learning for Biological Discovery -- Machine Learning in Bioinformatics -- Machine Learning in Genomics.
    Additional Edition: ISBN 3-031-39354-6
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 5
    UID:
    almafu_BV049640335
    Format: 1 Online-Ressource.
    ISBN: 978-3-031-39355-6
    Series Statement: Health informatics
    Additional Edition: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-031-39354-9
    Additional Edition: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-39357-0
    Language: English
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 6
    UID:
    almahu_9949709183402882
    Format: XXVI, 810 p. 146 illus., 130 illus. in color. , online resource.
    Edition: 1st ed. 2024.
    ISBN: 9783031393556
    Series Statement: Health Informatics,
    Content: 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.
    Note: Predictive Analytics -- Machine Learning -- Artificial Intelligence -- Data Mining -- Clinical Risk Models -- Clinical Risk Stratification -- Data Science -- Causal Discovery -- Causal Inference -- Causal Discovery in Health Sciences -- Causal Inference In Health Sciences -- Ehr Data Analytics -- Medical Knowledge Discovery -- Biomedical Machine Learning -- Biomedical Artificial Intelligence -- Healthcare Machine Learning -- Healthcare Artificial Intelligence -- Translational Science Machine Learning -- Machine Learning for Biological Discovery -- Machine Learning in Bioinformatics -- Machine Learning in Genomics.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783031393549
    Additional Edition: Printed edition: ISBN 9783031393563
    Additional Edition: Printed edition: ISBN 9783031393570
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 7
    UID:
    edocfu_9961442549102883
    Format: 1 online resource (XXVI, 810 p. 146 illus., 130 illus. in color.)
    Edition: 1st ed. 2024.
    ISBN: 3-031-39355-4
    Series Statement: Health Informatics,
    Content: 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.
    Note: Predictive Analytics -- Machine Learning -- Artificial Intelligence -- Data Mining -- Clinical Risk Models -- Clinical Risk Stratification -- Data Science -- Causal Discovery -- Causal Inference -- Causal Discovery in Health Sciences -- Causal Inference In Health Sciences -- Ehr Data Analytics -- Medical Knowledge Discovery -- Biomedical Machine Learning -- Biomedical Artificial Intelligence -- Healthcare Machine Learning -- Healthcare Artificial Intelligence -- Translational Science Machine Learning -- Machine Learning for Biological Discovery -- Machine Learning in Bioinformatics -- Machine Learning in Genomics.
    Additional Edition: ISBN 3-031-39354-6
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 8
    UID:
    edoccha_9961442549102883
    Format: 1 online resource (XXVI, 810 p. 146 illus., 130 illus. in color.)
    Edition: 1st ed. 2024.
    ISBN: 3-031-39355-4
    Series Statement: Health Informatics,
    Content: 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.
    Note: Predictive Analytics -- Machine Learning -- Artificial Intelligence -- Data Mining -- Clinical Risk Models -- Clinical Risk Stratification -- Data Science -- Causal Discovery -- Causal Inference -- Causal Discovery in Health Sciences -- Causal Inference In Health Sciences -- Ehr Data Analytics -- Medical Knowledge Discovery -- Biomedical Machine Learning -- Biomedical Artificial Intelligence -- Healthcare Machine Learning -- Healthcare Artificial Intelligence -- Translational Science Machine Learning -- Machine Learning for Biological Discovery -- Machine Learning in Bioinformatics -- Machine Learning in Genomics.
    Additional Edition: ISBN 3-031-39354-6
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 9
    UID:
    gbv_177918056X
    Format: 1 online resource (119 pages).
    Edition: First edition.
    ISBN: 9781498757317 , 1498757316 , 9780367806118 , 0367806118
    Series Statement: HIMSS book series
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 10
    UID:
    gbv_1885773781
    Format: 1 Online-Ressource (810 p.)
    ISBN: 9783031393556 , 9783031393549
    Series Statement: Health Informatics
    Content: 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
    Note: English
    Language: Undetermined
    Library Location Call Number Volume/Issue/Year Availability
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