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Titlebook: Knowledge Representation for Health-Care; ECAI 2010 Workshop K David Ria?o,Annette Teije,Mor Peleg Conference proceedings 2011 Springer Ber

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發(fā)表于 2025-3-21 18:07:20 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Knowledge Representation for Health-Care
副標(biāo)題ECAI 2010 Workshop K
編輯David Ria?o,Annette Teije,Mor Peleg
視頻videohttp://file.papertrans.cn/545/544033/544033.mp4
概述deals with the interaction between patient data and guidelines
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Knowledge Representation for Health-Care; ECAI 2010 Workshop K David Ria?o,Annette Teije,Mor Peleg Conference proceedings 2011 Springer Ber
描述This book constitutes the proceedings of the KR4HC 2010 workshop held at ECAI in Lisbon, Portugal, in August 2010. The 11 extended papers presented were carefully reviewed and selected from 19 submissions. The papers cover topics like ontologies, patient data, records, and guidelines, and clinical practice guidelines.
出版日期Conference proceedings 2011
關(guān)鍵詞cluster analysis; data mining; digital libraries; health care; health records; information systems; intero
版次1
doihttps://doi.org/10.1007/978-3-642-18050-7
isbn_softcover978-3-642-18049-1
isbn_ebook978-3-642-18050-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Berlin Heidelberg 2011
The information of publication is updating

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/k/image/544033.jpg
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Knowledge Representation for Health-Care978-3-642-18050-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
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0302-9743 in Lisbon, Portugal, in August 2010. The 11 extended papers presented were carefully reviewed and selected from 19 submissions. The papers cover topics like ontologies, patient data, records, and guidelines, and clinical practice guidelines.978-3-642-18049-1978-3-642-18050-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
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https://doi.org/10.1007/978-3-642-18050-7cluster analysis; data mining; digital libraries; health care; health records; information systems; intero
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Critiquing Knowledge Representation in Medical Image Interpretation Using Structure Learninge experiments we have gained much insight into the problem of knowledge representation and concluded that structure learning results can be conceptually clear and of help in designing a Bayesian network for medical image interpretation.
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Cristina Romero-Tris,David Ria?o,Francis Realed in the previous century to contain democratic unrest. This time, the idea was to undercut any possible appeal of the model taking shape in East Germany. The disadvantage of the system, however, was that it ran the risk of muting signals from the market and encouraging complacency.
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