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Natural Language Processing for Health-Related Texts

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Biomedical Informatics

Abstract

Narrative text is an important component of communication in health care, including patient-specific information in health record reports and notes and general biomedical knowledge papers, textbooks and web resources. Retrieval of information from such sources can be accomplished with keyword indexing but this approach fails to distinguish between texts that discuss a topic versus those that merely mention it. Natural language processing (NLP) techniques seek to analyze narrative text to make these distinctions and even to find instances where a concept is discussed but not explicitly mentioned. Previously, grammar-based methods were based on parsing sentences into their structure to infer semantics. Machine-learning methods are now being applied that can infer semantics through techniques such as statistical pattern recognition. Regular expression, rule bases, neural networks and word embeddings are among approaches that are improving the ability of automated systems to carry out successful language understanding, information extraction and question answering.

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Authors and Affiliations

  1. National Library of Medicine, Lister Hill National Center for Biomedical Communications, Bethesda, MD, USA

    Dina Demner-Fushman

  2. Department of Biomedical Informatics, Columbia University, New York, NY, USA

    Noémie Elhadad & Carol Friedman

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  1. Dina Demner-Fushman
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  2. Noémie Elhadad
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Correspondence to Noémie Elhadad .

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  1. Biomedical Informatics, Columbia University, New York, NY, USA

    Edward H. Shortliffe

  2. Informatics Institute, University of Alabama at Birmingham, Birmingham, AL, USA

    James J. Cimino

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Demner-Fushman, D., Elhadad, N., Friedman, C. (2021). Natural Language Processing for Health-Related Texts. In: Shortliffe, E.H., Cimino, J.J. (eds) Biomedical Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-58721-5_8

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