04074nam a22005775i 4500 978-981-97-3962-2 DE-He213 20251030112938.0 cr nn 008mamaa 240904s2024 si | s |||| 0|eng d 9789819739622 978-981-97-3962-2 10.1007/978-981-97-3962-2 doi QA76.9.D343 UNF bicssc UYQE bicssc COM021030 bisacsh UNF thema UYQE thema Text Mining Approaches for Biomedical Data [electronic resource] / edited by Aditi Sharan, Nidhi Malik, Hazra Imran, Indira Ghosh. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XV, 440 p. 166 illus., 123 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Transactions on Computer Systems and Networks, 2730-7492 Biomedical 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. The 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. Data mining. 6941 Application software. Data Mining and Knowledge Discovery. Computer and Information Systems Applications. Sharan, Aditi. editor. edt http://id.loc.gov/vocabulary/relators/edt 83736 Malik, Nidhi. editor. edt http://id.loc.gov/vocabulary/relators/edt 83737 Imran, Hazra. editor. edt http://id.loc.gov/vocabulary/relators/edt 83738 Ghosh, Indira. editor. edt http://id.loc.gov/vocabulary/relators/edt 83739 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819739615 Printed edition: 9789819739639 Printed edition: 9789819739646 Transactions on Computer Systems and Networks, 2730-7492 67020 https://doi.org/10.1007/978-981-97-3962-2 ZDB-2-ENG ZDB-2-SXE 63119 63119