This textbook covers broad topics within the application of natural language processing (NLP) in biomedicine, and provides in-depth review of the NLP solutions that reveal information embedded in biomedical text. The need for biomedical NLP research and development has grown rapidly in the past two decades as an important field in cognitive informatics. Natural Language Processing in Biomedicine: A Practical Guide introduces the history of the biomedical NLP field and takes the reader through the basic aspects of NLP including different levels of linguistic information and widely used machine learning and deep learning algorithms. The book details common biomedical NLP tasks, such as named entity recognition, concept normalization, relation extraction, text classification, information retrieval, and question answering. The book illustrates the tasks with real-life use cases and introduces real-world datasets, novel machine learning and deep learning algorithms, and large language models. Relevant resources for corpora and medical terminologies are also introduced. The final chapters are devoted to discussing applications of biomedical NLP in healthcare and life sciences. This textbook therefore represents essential reading for students in biomedical informatics programs, as well as for professionals who are conducting research or building biomedical NLP systems.

Uses a coherent design of chapters covering NLP methods, systems and applications, with useful examples and cases Contains pedagogical features such as learning objectives, glossaries, references, key readings and example questions Features schematics and illustrations to explain key points

Autorentext
Dr. Hua Xu is Robert T. McCluskey Professor and Vice Chair for Research and Development within the Section of Biomedical Informatics and Data Science at Yale School of Medicine. He received his Ph.D. in Biomedical Informatics from Columbia University. His primary research interests include biomedical natural language processing (NLP) and data mining, as well as their applications in secondary use of electronic health records data for clinical and translational research. His research is funded by multiple agencies (i.e., NLM, NCI, NIGMS, NIA, AHA, and NSF). NLP methods and tools developed in his lab have been top ranked in various biomedical NLP shared tasks and widely used to support diverse biomedical applications. He previously chaired the American Medical Informatics Association (AMIA) NLP Working Group and currently leads the Observational Health Data Sciences and Informatics (OHDSI) NLP Working Group. His contributions have been recognized with fellowships from both theAmerican College of Medical Informatics (ACMI) and the International Academy of Health Sciences Informatics (IAHSI).
Dr. Dina Demner-Fushmsn is an Investigator at the National Library of Medicine, NIH. Dr. Demner-Fushman leads research in the areas of Information Extraction for Clinical Decision Support, EMR Database Research and Development, and Image and Text Indexing for Clinical Decision Support and Education. The outgrowths of these projects are the evidence-based decision support system, InfoBot, used at the NIH Clinical Center from 2009 to 2020, an image retrieval engine, Open-i, launched in 2012, and an automatic question answering service CHiQA launched in 2018. Dr. Demner-Fushman authored more than 300 articles, book chapters and books in the fields of information retrieval, natural language processing, and biomedical and clinical informatics. Dr. Demner-Fushman is a Fellow of the American College of Medical Informatics (ACMI), an Associate Editor of the Journal of the American Medical Informatics Association, a member of Nature's Scientific Data Editorial Board, past chair of the AMIA NLP SIG, and a founding member and chair of the Association for Computational Linguistics (ACL) Special Interest Group on biomedical natural language processing.

Inhalt
Introduction.- Overview of linguistic information.- Deal with words.- Processing sentences.- Corpus analysis.- Machine learning and deep learning algorithms.- Named entity recognition.- Relation extraction.- Concept normalization (entity linking).- Information retrieval.- Text classification.- Question answering.- Text generation.- Developing Biomedical NLP Systems.- NLP applications in healthcare.- NLP applications for life science.