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.

Highlights textual data in biomedical and health databases, literature, clinical trials Focuses on further application of text mining techniques Discusses building descriptive and predictive models and their evaluation

Autorentext

Dr. Aditi Sharan is an Associate Professor at the School of Computers and Systems Sciences, Jawaharlal Nehru University (JNU), India. She has over 25 years of teaching and research experience. She has taught natural language processing, machine learning, web mining, and artificial intelligence at the postgraduate level, along with fundamental subjects of computer science. She has been actively engaged in research and supervision at the M.Tech. and Ph.D. levels for around 20 years. She has published papers in journals and conferences of national and international repute along with invited talks within and outside India. Her research interests include text mining, web mining, natural language processing, information retrieval, sentiment analysis, artificial intelligence, and machine learning. Currently, her focus is on Biomedical text mining. She is interested in collaborations with people working in the biomedical and health domain.  

Dr. Nidhi Malik is a computer science faculty at The NorthCap University, Gurugram. She has over 13 years of experience in academia and research. She completed her Ph.D. from Jawaharlal Nehru University in 2017. She has published several research papers in conferences and journals of national and international repute. Her research interests include Artificial Intelligence, Semantic Web Technologies, and Natural Language Processing.

Dr. Hazra Imran is an Associate Teaching Professor at Northeastern University in Vancouver, British Columbia, Canada. With a diverse teaching background, she has instructed numerous computer science courses across institutions in both Canada and India.  Dr. Imran earned her Ph.D. in Information Retrieval from Jawaharlal Nehru University (India) and completed her postdoctoral fellowship at Athabasca University (Canada). With over 18 years of experience in academia and research, she has contributed extensively to journals and conferences of national and international acclaim. Her research interests span information retrieval, semantic web, data mining, recommender systems, and machine learning.

Prof. Indira Ghosh has served as Professor and Dean at SCIS, Jawaharlal Nehru University (JNU), India (2008-2019). She is one of the leading scientists in Computational Biology and Systems Biology. She has also offered clinical research and data management courses at Pune University, India, during 2006-2010. She has both industry (AstraZeneca 1990-2003) and academic experience with many publications in drug design, SW & databases in bioinformatics.  

Inhalt

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.