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Artificial intelligence and machine learning in health care and medical sciences = best practices and pitfalls /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence and machine learning in health care and medical sciences/ edited by Gyorgy J. Simon, Constantin Aliferis.
Reminder of title:	best practices and pitfalls /
other author:	Simon, Gyorgy J.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xxvi, 810 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence - Medical applications. -
Online resource:	https://doi.org/10.1007/978-3-031-39355-6
ISBN:	9783031393556

Artificial intelligence and machine learning in health care and medical sciences = best practices and pitfalls /
Artificial intelligence and machine learning in health care and medical sciencesbest practices and pitfalls /[electronic resource] :edited by Gyorgy J. Simon, Constantin Aliferis. - Cham :Springer International Publishing :2024. - xxvi, 810 p. :ill. (chiefly col.), digital ;24 cm. - Health informatics,2197-3741. - Health informatics..

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.

Open access.

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.

ISBN: 9783031393556

Standard No.: 10.1007/978-3-031-39355-6doiSubjects--Topical Terms:

900591
Artificial intelligence
--Medical applications.

LC Class. No.: R859.7.A78

Dewey Class. No.: 610.285

Artificial intelligence and machine learning in health care and medical sciences = best practices and pitfalls /
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505 0 $a 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.
506 $a Open access.
520 $a 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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