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Titlebook: Ventricular-Assist Devices and Kidney Disease; Clinical Perspective Chaitanya Desai,William G. Cotts,Michael R. Rudnic Book 2018 Springer I

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书目名称Ventricular-Assist Devices and Kidney Disease
副标题Clinical Perspective
编辑Chaitanya Desai,William G. Cotts,Michael R. Rudnic
视频videohttp://file.papertrans.cn/982/981039/981039.mp4
概述Covers kidney diseases in context of advanced heart failure, presence of ventricular assist devices and heart transplantation.Provides nephrologists, cardiologists, transplant specialists and speciali
图书封面Titlebook: Ventricular-Assist Devices and Kidney Disease; Clinical Perspective Chaitanya Desai,William G. Cotts,Michael R. Rudnic Book 2018 Springer I
描述.This text serves as a convenient one-stop resource on the pathogenesis, diagnosis and treatment of kidney diseases in context of advanced heart failure and presence of ventricular assist devices. Advances in managing heart failure, evidence-based practices and therapies are covered, along with the management of this unique and complex subset of patients. The book also provides insights into combined heart and kidney transplantation. Authored by leaders in nephrology, cardiology and transplantation, this book fills a critical knowledge gap in understanding and treating patients with acute or chronic dysfunction of the heart and kidneys and how one induces dysfunction in the other organ..
出版日期Book 2018
关键词Left Ventricular Assist Device LVAD; Chronic Kidney Disease CKD; advanced heart failure; cardiovascular
版次1
doihttps://doi.org/10.1007/978-3-319-74657-9
isbn_softcover978-3-030-09046-3
isbn_ebook978-3-319-74657-9
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

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Amanda K. Leonberg-Yoo,J. Eduardo Rame,Michael R. Rudnickesearchand applications, encompassing the fac- itating theoretical foundations and the innovative applications of such results in other areas.978-3-540-34075-1978-3-540-34076-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
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n small training sets, while bag-of-words and TF-IDF presents better results on large training sets. In this work, we propose a hybrid model that combines TF-IDF and word embedding in order to provide the answer type to text questions using small and large training sets. Our experiments using the Po
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