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Abstract

Extracting relations among medical concepts is very important in the medical field. The relations denote the events or the possible relations between the concepts. Information about these relations provides users with a full view of medical problems. This helps physicians and health-care practitioners make effective decisions and minimize errors in the treatment process. This paper collects methods for relations extraction in health texts and presents an approach on one type of specific relation (i.e. template filling). The approach combines methods including rule-based and machine learningbased. The rule-based method uses the relation of semantic dependencies among the concepts to extract the rule set. The machine learning-based method uses the SVM (Support Vector Machine) algorithm and a feature set proposed. The results of the approach were estimated on an accuracy of 0.849.



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Article Details

Issue: Vol 1 No Q3 (2017)
Page No.: 51-63
Published: Dec 31, 2017
Section: Research article
DOI: https://doi.org/10.32508/stdjelm.v1iQ3.449

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Creative Commons License

Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 How to Cite
Huynh, N., Ho, Q., & Nguyen, T. (2017). An approach in health relation extraction. Science & Technology Development Journal: Economics- Law & Management, 1(Q3), 51-63. https://doi.org/https://doi.org/10.32508/stdjelm.v1iQ3.449

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