Biomedical Data Mining for Information Retrieval

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Scrivener Publishing100 Cummings Center, Suite 541J Beverly, MA 01915-6106

Artificial Intelligence and Soft Computing for Industrial Transformation

Series Editor: Dr S. Balamurugan (sbnbala@gmail.com)

Scope: Artificial Intelligence and Soft Computing Techniques play an impeccable role in industrial transformation. The topics to be covered in this book series include Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks, Fuzzy Logic, Genetic Algorithms, Particle Swarm Optimization, Evolutionary Algorithms, Nature Inspired Algorithms, Simulated Annealing, Metaheuristics, Cuckoo Search, Firefly Optimization, Bio-inspired Algorithms, Ant Colony Optimization, Heuristic Search Techniques, Reinforcement Learning, Inductive Learning, Statistical Learning, Supervised and Unsupervised Learning, Association Learning and Clustering, Reasoning, Support Vector Machine, Di˙erential Evolution Algorithms, Expert Systems, Neuro Fuzzy Hybrid Systems, Genetic Neuro Hybrid Systems, Genetic Fuzzy Hybrid Systems and other Hybridized So~ Computing Techniques and their applications for Industrial Transformation. The book series is aimed to provide comprehensive handbooks and reference books for the benefit of scientists, research scholars, students and industry professional working towards next generation industrial transformation.

Publishers at Scrivener Martin Scrivener ( martin@scrivenerpublishing.com) Phillip Carmical ( pcarmical@scrivenerpublishing.com)

Edited by

Sujata Dash,

Subhendu Kumar Pani,

S. Balamurugan

and

Ajith Abraham

This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-71124-7

Cover image: Pixabay.Com

Cover design by Russell Richardson

Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

Printed in the USA

10 9 8 7 6 5 4 3 2 1

Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval, which is an emerging research field at the intersection of information science and computer science. Biomedical and health informatics is another remerging field of research at the intersection of information science, computer science and healthcare. This new era of healthcare informatics and analytics brings with it tremendous opportunities and challenges based on the abundance of biomedical data easily available for further analysis. The aim of healthcare informatics is to ensure high-quality, efficient healthcare and better treatment and quality of life by efficiently analyzing biomedical and healthcare data, including patients’ data, electronic health records (EHRs) and lifestyle. Earlier, it was commonly required to have a domain expert develop a model for biomedical or healthcare data; however, recent advancements in representation learning algorithms allow automatic learning of the pattern and representation of given data for the development of such a model. Biomedical image mining is a novel research area brought about by the large number of biomedical images increasingly being generated and stored digitally. These images are mainly generated by 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 critical questions related to their healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can aid doctors in treating their patients.