A semi-supervised approach for extracting TCM clinical terms based on feature words

BACKGROUND: A semi-supervised model is proposed for extracting clinical terms of Traditional Chinese Medicine using feature words. METHODS: The extraction model is based on BiLSTM-CRF and combined with semi-supervised learning and feature word set, which reduces the cost of manual annotation and lev...

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Autores principales: Liu, Liangliang, Wu, Xiaojing, Liu, Hui, Cao, Xinyu, Wang, Haitao, Zhou, Hongwei, Xie, Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7477860/
https://www.ncbi.nlm.nih.gov/pubmed/32646408
http://dx.doi.org/10.1186/s12911-020-1108-1
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author Liu, Liangliang
Wu, Xiaojing
Liu, Hui
Cao, Xinyu
Wang, Haitao
Zhou, Hongwei
Xie, Qi
author_facet Liu, Liangliang
Wu, Xiaojing
Liu, Hui
Cao, Xinyu
Wang, Haitao
Zhou, Hongwei
Xie, Qi
author_sort Liu, Liangliang
collection PubMed
description BACKGROUND: A semi-supervised model is proposed for extracting clinical terms of Traditional Chinese Medicine using feature words. METHODS: The extraction model is based on BiLSTM-CRF and combined with semi-supervised learning and feature word set, which reduces the cost of manual annotation and leverage extraction results. RESULTS: Experiment results show that the proposed model improves the extraction of five types of TCM clinical terms, including traditional Chinese medicine, symptoms, patterns, diseases and formulas. The best F1-value of the experiment reaches 78.70% on the test dataset. CONCLUSIONS: This method can reduce the cost of manual labeling and improve the result in the NER research of TCM clinical terms.
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spelling pubmed-74778602020-09-09 A semi-supervised approach for extracting TCM clinical terms based on feature words Liu, Liangliang Wu, Xiaojing Liu, Hui Cao, Xinyu Wang, Haitao Zhou, Hongwei Xie, Qi BMC Med Inform Decis Mak Research BACKGROUND: A semi-supervised model is proposed for extracting clinical terms of Traditional Chinese Medicine using feature words. METHODS: The extraction model is based on BiLSTM-CRF and combined with semi-supervised learning and feature word set, which reduces the cost of manual annotation and leverage extraction results. RESULTS: Experiment results show that the proposed model improves the extraction of five types of TCM clinical terms, including traditional Chinese medicine, symptoms, patterns, diseases and formulas. The best F1-value of the experiment reaches 78.70% on the test dataset. CONCLUSIONS: This method can reduce the cost of manual labeling and improve the result in the NER research of TCM clinical terms. BioMed Central 2020-07-09 /pmc/articles/PMC7477860/ /pubmed/32646408 http://dx.doi.org/10.1186/s12911-020-1108-1 Text en © The Author(s). 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Liu, Liangliang
Wu, Xiaojing
Liu, Hui
Cao, Xinyu
Wang, Haitao
Zhou, Hongwei
Xie, Qi
A semi-supervised approach for extracting TCM clinical terms based on feature words
title A semi-supervised approach for extracting TCM clinical terms based on feature words
title_full A semi-supervised approach for extracting TCM clinical terms based on feature words
title_fullStr A semi-supervised approach for extracting TCM clinical terms based on feature words
title_full_unstemmed A semi-supervised approach for extracting TCM clinical terms based on feature words
title_short A semi-supervised approach for extracting TCM clinical terms based on feature words
title_sort semi-supervised approach for extracting tcm clinical terms based on feature words
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7477860/
https://www.ncbi.nlm.nih.gov/pubmed/32646408
http://dx.doi.org/10.1186/s12911-020-1108-1
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