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Artificial Intelligence and Machine Learning in Health Care and Medical Sciences
Best Practices and Pitfalls
Buch von Gyorgy J. Simon (u. a.)
Sprache: Englisch

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Beschreibung
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.

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.

Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2024
Fachbereich: Allgemeine Lexika
Genre: Medizin
Rubrik: Wissenschaften
Medium: Buch
Inhalt: xxvi
810 S.
16 s/w Illustr.
130 farbige Illustr.
810 p. 146 illus.
130 illus. in color.
ISBN-13: 9783031393549
ISBN-10: 3031393546
Sprache: Englisch
Herstellernummer: 978-3-031-39354-9
Redaktion: Simon, Gyorgy J.
Aliferis, Constantin
Herausgeber: Gyorgy J Simon/Constantin Aliferis
Hersteller: Springer
Springer, Berlin
Institute for Health Informatics
Springer International Publishing
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Abbildungen: XXVI, 810 p. 146 illus., 130 illus. in color.
Maße: 42 x 162 x 243 mm
Von/Mit: Gyorgy J. Simon (u. a.)
Erscheinungsdatum: 05.03.2024
Gewicht: 1,708 kg
Artikel-ID: 127200757
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2024
Fachbereich: Allgemeine Lexika
Genre: Medizin
Rubrik: Wissenschaften
Medium: Buch
Inhalt: xxvi
810 S.
16 s/w Illustr.
130 farbige Illustr.
810 p. 146 illus.
130 illus. in color.
ISBN-13: 9783031393549
ISBN-10: 3031393546
Sprache: Englisch
Herstellernummer: 978-3-031-39354-9
Redaktion: Simon, Gyorgy J.
Aliferis, Constantin
Herausgeber: Gyorgy J Simon/Constantin Aliferis
Hersteller: Springer
Springer, Berlin
Institute for Health Informatics
Springer International Publishing
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Abbildungen: XXVI, 810 p. 146 illus., 130 illus. in color.
Maße: 42 x 162 x 243 mm
Von/Mit: Gyorgy J. Simon (u. a.)
Erscheinungsdatum: 05.03.2024
Gewicht: 1,708 kg
Artikel-ID: 127200757
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