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Text mining approaches for biomedica...

Sharan, Aditi.

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Text mining approaches for biomedical data

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Text mining approaches for biomedical data/ edited by Aditi Sharan ...[et al.].
其他作者:	Sharan, Aditi.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xv, 440 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Medical informatics - Data processing. -
電子資源:	https://doi.org/10.1007/978-981-97-3962-2
ISBN:	9789819739622

Text mining approaches for biomedical data
Text mining approaches for biomedical data[electronic resource] /edited by Aditi Sharan ...[et al.]. - Singapore :Springer Nature Singapore :2024. - xv, 440 p. :ill. (some col.), digital ;24 cm. - Transactions on computer systems and networks,2730-7492. - Transactions on computer systems and networks..

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.

ISBN: 9789819739622

Standard No.: 10.1007/978-981-97-3962-2doiSubjects--Topical Terms:

3620791
Medical informatics
--Data processing.

LC Class. No.: R859.7.D35

Dewey Class. No.: 610.285

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520 $a 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.
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