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020 _a9789819739622
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024 7 _a10.1007/978-981-97-3962-2
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
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072 7 _aCOM021030
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072 7 _aUNF
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245 1 0 _aText Mining Approaches for Biomedical Data
_h[electronic resource] /
_cedited by Aditi Sharan, Nidhi Malik, Hazra Imran, Indira Ghosh.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXV, 440 p. 166 illus., 123 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 _aTransactions on Computer Systems and Networks,
_x2730-7492
505 0 _aBiomedical Data Types, Sources, Content and Retrieval -- Information Analysis using Biomedical text mining -- Connection and Curation of Corpus (Labeled and Unlabeled) -- Biomedical Data Visualization -- Biomedical Text data visualization -- Role of Ontology in Biomedical text mining -- Ontology in Text mining and matching -- Fundamentals of Vector-Based Text Representation and Word Embeddings -- Transformer-based Models for Text Representation and Processing -- Information Retrieval and Query Expansion for Biomedical Data -- Advances in Biomedical Entity and Relation Extraction: Techniques and Applications.
520 _aThe book 'Text Mining Approaches for Biomedical Data' delves into the fascinating realm of text mining in healthcare. It provides an in-depth understanding of how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing healthcare research and patient care. The book covers a wide range of topics such as mining textual data in biomedical and health databases, analyzing literature and clinical trials, and demonstrating various applications of text mining in healthcare. This book is a guide for effectively representing textual data using vectors, knowledge graphs, and other advanced techniques. It covers various text mining applications, building descriptive and predictive models, and evaluating them. Additionally, it includes building machine learning models using textual data, covering statistical and deep learning approaches. This book is designed to be a valuable reference for computer science professionals, researchers in the biomedical field, and clinicians. It provides practical guidance and promotes collaboration between different disciplines. Therefore, it is a must-read for anyone who is interested in the intersection of text mining and healthcare.
650 0 _aData mining.
_96941
650 0 _aApplication software.
650 1 4 _aData Mining and Knowledge Discovery.
650 2 4 _aComputer and Information Systems Applications.
700 1 _aSharan, Aditi.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_983736
700 1 _aMalik, Nidhi.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_983737
700 1 _aImran, Hazra.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_983738
700 1 _aGhosh, Indira.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_983739
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9789819739639
776 0 8 _iPrinted edition:
_z9789819739646
830 0 _aTransactions on Computer Systems and Networks,
_x2730-7492
_967020
856 4 0 _uhttps://doi.org/10.1007/978-981-97-3962-2
912 _aZDB-2-ENG
912 _aZDB-2-SXE
999 _c63119
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