logo

EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link:  https://ebookbell.com/faq 


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Text Mining Approaches For Biomedical Data Aditi Sharan Nidhi Malik

  • SKU: BELL-59717774
Text Mining Approaches For Biomedical Data Aditi Sharan Nidhi Malik
$ 31.00 $ 45.00 (-31%)

4.7

96 reviews

Text Mining Approaches For Biomedical Data Aditi Sharan Nidhi Malik instant download after payment.

Publisher: Springer
File Extension: PDF
File size: 31.09 MB
Pages: 455
Author: Aditi Sharan, Nidhi Malik, Hazra Imran, Indira Ghosh
ISBN: 9789819739615, 9819739616
Language: English
Year: 2024

Product desciption

Text Mining Approaches For Biomedical Data Aditi Sharan Nidhi Malik by Aditi Sharan, Nidhi Malik, Hazra Imran, Indira Ghosh 9789819739615, 9819739616 instant download after payment.

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.

Related Products