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Titlebook: Computational Medicine in Data Mining and Modeling; Goran Rakocevic,Tijana Djukic,Veljko Milutinovi? Book 2013 Springer Science+Business M

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書目名稱Computational Medicine in Data Mining and Modeling
編輯Goran Rakocevic,Tijana Djukic,Veljko Milutinovi?
視頻videohttp://file.papertrans.cn/233/232685/232685.mp4
概述Explains the latest cross-disciplinary research based on synergy of results that can be obtained with the different described approaches.Demonstrates applications of data mining to the medical domain.
圖書封面Titlebook: Computational Medicine in Data Mining and Modeling;  Goran Rakocevic,Tijana Djukic,Veljko Milutinovi? Book 2013 Springer Science+Business M
描述This book presents an overview of a variety of contemporary statistical, mathematical and computer science techniques which are used to further the knowledge in the medical domain. The authors focus on applying data mining to the medical domain, including mining the sets of clinical data typically found in patient’s medical records, image mining, medical mining, data mining and machine learning applied to generic genomic data and more.This work also introduces modeling behavior of cancer cells, multi-scale computational models and simulations of blood flow through vessels by using patient-specific models. The authors cover different imaging techniques used to generate patient-specific models. This is used in computational fluid dynamics software to analyze fluid flow. Case studies are provided at the end of each chapter.Professionals and researchers with quantitative backgrounds will find Computational Medicine in Data Mining and Modeling useful as a reference. Advanced-level students studying computer science, mathematics, statistics and biomedicine will also find this book valuable as a reference or secondary text book.
出版日期Book 2013
關(guān)鍵詞Biomedical Modeling; Computation in Medicine; Imaging Reconstruction; Medical Data Mining; Patient-Speci
版次1
doihttps://doi.org/10.1007/978-1-4614-8785-2
isbn_softcover978-1-4939-4834-5
isbn_ebook978-1-4614-8785-2
copyrightSpringer Science+Business Media New York 2013
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Book 2013owledge in the medical domain. The authors focus on applying data mining to the medical domain, including mining the sets of clinical data typically found in patient’s medical records, image mining, medical mining, data mining and machine learning applied to generic genomic data and more.This work a
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https://doi.org/10.1007/978-0-387-73251-0 or “severely progressed,” from a second group containing “mildly diseased” or “mildly progressed” patients, respectively. This latter mild/severe characterization is the actual value of the target variable for each patient.
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978-1-4939-4834-5Springer Science+Business Media New York 2013
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