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020 _a9783031558658
_9978-3-031-55865-8
024 7 _a10.1007/978-3-031-55865-8
_2doi
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072 7 _aMBG
_2bicssc
072 7 _aUB
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072 7 _aMED117000
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245 1 0 _aNatural Language Processing in Biomedicine
_h[electronic resource] :
_bA Practical Guide /
_cedited by Hua Xu, Dina Demner Fushman.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXVI, 444 p. 75 illus., 42 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aCognitive Informatics in Biomedicine and Healthcare,
_x2662-7299
505 0 _aIntroduction -- Overview of linguistic information -- Deal with words -- Processing sentences -- Corpus analysis -- Machine learning and deep learning algorithms -- Named entity recognition -- Relation extraction -- Concept normalization (entity linking) -- Information retrieval -- Text classification -- Question answering -- Text generation -- Developing Biomedical NLP Systems -- NLP applications in healthcare -- NLP applications for life science.
520 _aThis 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 textbook therefore represents essential reading for students in biomedical informatics programs, as well as for professionals who are conducting research or building biomedical NLP systems.
650 0 _aMedical informatics.
_960693
650 0 _aPsycholinguistics.
_972377
650 1 4 _aHealth Informatics.
650 2 4 _aLanguage Processing.
_978450
700 1 _aXu, Hua.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_970439
700 1 _aDemner Fushman, Dina.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_978451
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031558641
776 0 8 _iPrinted edition:
_z9783031558665
776 0 8 _iPrinted edition:
_z9783031558672
830 0 _aCognitive Informatics in Biomedicine and Healthcare,
_x2662-7299
_978452
856 4 0 _uhttps://doi.org/10.1007/978-3-031-55865-8
912 _aZDB-2-SME
912 _aZDB-2-SXM
999 _c60609
_d60609