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dc.contributor.editorSimon, Gyorgy J.
dc.contributor.editorAliferis, Constantin
dc.date.accessioned2024-03-14T04:12:26Z
dc.date.available2024-03-14T04:12:26Z
dc.date.issued2024
dc.date.submitted2024-03-13T11:09:52Z
dc.identifierONIX_20240313_9783031393556_10
dc.identifierhttps://library.oapen.org/handle/20.500.12657/88301
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/135621
dc.description.abstractThis 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.
dc.languageEnglish
dc.relation.ispartofseriesHealth Informatics
dc.rightsopen access
dc.subject.otherPredictive analytics
dc.subject.otherArtificial intelligence
dc.subject.otherMedicine
dc.subject.otherMachine learning
dc.subject.otherCausal discovery
dc.subject.otherCausal inference
dc.subject.otherGenomics
dc.subject.otherMedical knowledge discovery
dc.subject.otherClinical risk models
dc.subject.otherClinical risk stratification
dc.subject.otherthema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBG Medical equipment and techniques
dc.subject.otherthema EDItEUR::U Computing and Information Technology::UB Information technology: general topics
dc.subject.otherthema EDItEUR::M Medicine and Nursing::MQ Nursing and ancillary services
dc.subject.otherthema EDItEUR::U Computing and Information Technology::UY Computer science
dc.subject.otherthema EDItEUR::P Mathematics and Science::PS Biology, life sciences
dc.subject.otherthema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine
dc.titleArtificial Intelligence and Machine Learning in Health Care and Medical Sciences
dc.title.alternativeBest Practices and Pitfalls
dc.typebook
oapen.identifier.doi10.1007/978-3-031-39355-6
oapen.relation.isPublishedBy9fa3421d-f917-4153-b9ab-fc337c396b5a
oapen.relation.isFundedByea8fb1dd-6656-4566-8f9b-3aea90ff5e8a
oapen.relation.isbn9783031393556
oapen.relation.isbn9783031393549
oapen.imprintSpringer International Publishing
oapen.pages810
oapen.place.publicationCham
oapen.grant.number[...]
dc.relationisFundedByea8fb1dd-6656-4566-8f9b-3aea90ff5e8a


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