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Titlebook: Natural Language Processing in Biomedicine; A Practical Guide Hua Xu,Dina Demner Fushman Textbook 2024 The Editor(s) (if applicable) and Th

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發(fā)表于 2025-3-21 18:43:57 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Natural Language Processing in Biomedicine
副標(biāo)題A Practical Guide
編輯Hua Xu,Dina Demner Fushman
視頻videohttp://file.papertrans.cn/662/661849/661849.mp4
概述Uses a coherent design of chapters covering NLP methods, systems and applications, with useful examples and cases.Contains pedagogical features such as learning objectives, glossaries, references, key
叢書(shū)名稱Cognitive Informatics in Biomedicine and Healthcare
圖書(shū)封面Titlebook: Natural Language Processing in Biomedicine; A Practical Guide Hua Xu,Dina Demner Fushman Textbook 2024 The Editor(s) (if applicable) and Th
描述This textbook covers broad topics within the application of natural language processing (NLP) in biomedicine, and provides in-depth review of the NLP solutions that reveal information embedded in biomedical text. The need for biomedical NLP research and development has grown rapidly in the past two decades as an important field in cognitive informatics..?.Natural Language Processing in Biomedicine: A Practical Guide.?introduces the history of the biomedical NLP field and takes the reader through the basic aspects of NLP including different levels of linguistic information and widely used machine learning and deep learning algorithms. The book details common biomedical NLP tasks, such as named entity recognition, concept normalization, relation extraction, text classification, information retrieval, and question answering. The book illustrates the tasks with real-life use cases and introduces real-world datasets, novel machine learning and deep learning algorithms, and large language models. Relevant resources for corpora and medical terminologies are also introduced. The final chapters are devoted to discussing applications of biomedical NLP in healthcare and life sciences. This te
出版日期Textbook 2024
關(guān)鍵詞natural language processing; Biomedical NLP; Biomedical text processing; Biomedical text mining; Machine
版次1
doihttps://doi.org/10.1007/978-3-031-55865-8
isbn_softcover978-3-031-55867-2
isbn_ebook978-3-031-55865-8Series ISSN 2662-7280 Series E-ISSN 2662-7299
issn_series 2662-7280
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
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Relation ExtractionP). The emphasis here is on discussing the challenges and relevancy of the relation extraction task to biomedical text. The chapter provides references to the existing studies, approaches used, and available datasets for understanding and further developing new relation extraction systems.
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Medical Concept Normalizationtility by presenting a compendium of available resources, including shared tasks and annotated corpora specifically tailored to concept normalization, to empower and streamline the endeavors of readers engaged in this specialized field of research.
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發(fā)表于 2025-3-22 20:54:25 | 只看該作者
Text Classificationons between some challenging aspects of biomedical text processing and representational choices with the capacity to address these challenges. In addition it provides some worked examples of applications of text categorization techniques to clinical problems.
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Textbook 2024solutions that reveal information embedded in biomedical text. The need for biomedical NLP research and development has grown rapidly in the past two decades as an important field in cognitive informatics..?.Natural Language Processing in Biomedicine: A Practical Guide.?introduces the history of the
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發(fā)表于 2025-3-23 09:30:00 | 只看該作者
Overview of Linguistic Informationguistic topics, and ties the well-known linguistic notions to clinical text, while emphasizing the importance of linguistic knowledge in clinical text processing. The chapter starts with the description of medical sublanguages.
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