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Application of Machine Learning Techniques in Clinical Information Extraction

  • Ruchi PatelEmail author
  • Sanjay Tanwani
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 374)

Abstract

A large number of medical research papers and clinical notes on disease diagnostic, treatment and prevention are increasing every day. This biomedical text provides a rich source of knowledge for biomedical research. However, this medical information is scattered in vast medical informatics literature in unstructured form. It is requisite to retrieve imperative information from these publications and discover new knowledge. A lot of research is done in biomedical text mining using different methods and techniques. Centre of i2b2 organized different challenges on natural language processing for medical text. In i2b2 2010, challenge tasks were focused on concept extraction, assertion classification and relation extraction, and in 2012, the task was temporal information extraction. In previous work, various machine learning techniques are found to be one of the effective techniques to extract clinical information from different types of medical data like discharge summary, physical notes. This paper presents the review of earlier work on different machine learning techniques and methods for medical research. The effectiveness of these techniques has been measured by precision, recall and F-score. This review will be useful for biomedical researchers to identify best techniques for the further research in clinical information extraction.

Keywords

Concept extraction Assertion classification Relation extraction Temporal information extraction I2b2 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and ITDAVVIndoreIndia

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