Biomedical Data Mining for Information Retrieval

This book PDF is perfect for those who love Computers genre, written by Sujata Dash and published by John Wiley & Sons which was released on 24 August 2021 with total hardcover pages 450. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Biomedical Data Mining for Information Retrieval books below.

Biomedical Data Mining for Information Retrieval
Biomedical Data Mining for Information Retrieval
Author : Sujata Dash
File Size : 55,7 Mb
Publisher : John Wiley & Sons
Language : English
Release Date : 24 August 2021
ISBN : 9781119711247
Pages : 450 pages
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Biomedical Data Mining for Information Retrieval by Sujata Dash Book PDF Summary

BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.

Biomedical Data Mining for Information Retrieval

Biomedical Data Mining for Information Retrieval by Sujata Dash,Subhendu Kumar Pani,S. Balamurugan,Ajith Abraham

BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information

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Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

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Comprehensively presents the foundations and leading application research in medical informatics/biomedicine. The concepts and techniques are illustrated with detailed case studies. Authors are widely recognized professors and researchers in Schools of Medicine and Information Systems from the University of Arizona, University of Washington, Columbia University, and Oregon Health & Science

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Information Retrieval

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This book constitutes the refereed proceedings of the International Workshop on Data Mining for Biomedical Applications, BioDM 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). The 14 revised full papers presented together with one keynote talk were carefully reviewed and selected from 35

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