Introduction

 With an enormous amount of biomedical text data being generated from multiple sources, a lot of knowledge remains hidden in the debris of the text. Text mining is emerging as a field for retrieving and extracting meaningful patterns and knowledge hidden in the text to satisfy a user’s needs. In general, text mining approaches are generic that can be applied to the text of different domains, however the domain focused knowledge if used and integrated appropriately with text mining models, can greatly enhance the performance of these models. Specifically, a lot of domain knowledge is available in digital form in biomedical domain that remains underutilized and needs to be integrated with text mining models. The book ‘Text Mining approaches for Biomedical Data’ provides an in-depth understanding of how Artificial Intelligence and Machine Learning approaches can be integrated with biomedical domain knowledge to improve the efficiency of machine learning models. The integration can revolutionize healthcare research and patient care. The book focuses on biomedical aspects of text data, evolution of text representation models, basic tasks associated with text mining, emergence of biomedical knowledge graphs as a tool for text mining and some popular applications of biomedical text mining. The book will be useful for the students/researchers/professional of both computer science and biomedical domain interested in the field of biomedical text mining. It can also be used as a textbook for text mining or biomedical text mining course. Also, it is useful for researchers working in field of biomedical domain, those working for clinical trials as well as medical practitioners.