Anatomical entity recognition with a hierarchical framework augmented by external resources.

References to anatomical entities in medical records consist not only of explicit references to anatomical locations, but also other diverse types of expressions, such as specific diseases, clinical tests, clinical treatments, which constitute implicit references to anatomical entities. In order to...

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Autores principales:	Yan Xu, Ji Hua, Zhaoheng Ni, Qinlang Chen, Yubo Fan, Sophia Ananiadou, Eric I-Chao Chang, Junichi Tsujii
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/975b50d13a40431eb61ed4a8d98c312c
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id	oai:doaj.org-article:975b50d13a40431eb61ed4a8d98c312c
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spelling	oai:doaj.org-article:975b50d13a40431eb61ed4a8d98c312c2021-11-25T05:55:20ZAnatomical entity recognition with a hierarchical framework augmented by external resources.1932-620310.1371/journal.pone.0108396https://doaj.org/article/975b50d13a40431eb61ed4a8d98c312c2014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0108396https://doaj.org/toc/1932-6203References to anatomical entities in medical records consist not only of explicit references to anatomical locations, but also other diverse types of expressions, such as specific diseases, clinical tests, clinical treatments, which constitute implicit references to anatomical entities. In order to identify these implicit anatomical entities, we propose a hierarchical framework, in which two layers of named entity recognizers (NERs) work in a cooperative manner. Each of the NERs is implemented using the Conditional Random Fields (CRF) model, which use a range of external resources to generate features. We constructed a dictionary of anatomical entity expressions by exploiting four existing resources, i.e., UMLS, MeSH, RadLex and BodyPart3D, and supplemented information from two external knowledge bases, i.e., Wikipedia and WordNet, to improve inference of anatomical entities from implicit expressions. Experiments conducted on 300 discharge summaries showed a micro-averaged performance of 0.8509 Precision, 0.7796 Recall and 0.8137 F1 for explicit anatomical entity recognition, and 0.8695 Precision, 0.6893 Recall and 0.7690 F1 for implicit anatomical entity recognition. The use of the hierarchical framework, which combines the recognition of named entities of various types (diseases, clinical tests, treatments) with information embedded in external knowledge bases, resulted in a 5.08% increment in F1. The resources constructed for this research will be made publicly available.Yan XuJi HuaZhaoheng NiQinlang ChenYubo FanSophia AnaniadouEric I-Chao ChangJunichi TsujiiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 10, p e108396 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yan Xu Ji Hua Zhaoheng Ni Qinlang Chen Yubo Fan Sophia Ananiadou Eric I-Chao Chang Junichi Tsujii Anatomical entity recognition with a hierarchical framework augmented by external resources.
description	References to anatomical entities in medical records consist not only of explicit references to anatomical locations, but also other diverse types of expressions, such as specific diseases, clinical tests, clinical treatments, which constitute implicit references to anatomical entities. In order to identify these implicit anatomical entities, we propose a hierarchical framework, in which two layers of named entity recognizers (NERs) work in a cooperative manner. Each of the NERs is implemented using the Conditional Random Fields (CRF) model, which use a range of external resources to generate features. We constructed a dictionary of anatomical entity expressions by exploiting four existing resources, i.e., UMLS, MeSH, RadLex and BodyPart3D, and supplemented information from two external knowledge bases, i.e., Wikipedia and WordNet, to improve inference of anatomical entities from implicit expressions. Experiments conducted on 300 discharge summaries showed a micro-averaged performance of 0.8509 Precision, 0.7796 Recall and 0.8137 F1 for explicit anatomical entity recognition, and 0.8695 Precision, 0.6893 Recall and 0.7690 F1 for implicit anatomical entity recognition. The use of the hierarchical framework, which combines the recognition of named entities of various types (diseases, clinical tests, treatments) with information embedded in external knowledge bases, resulted in a 5.08% increment in F1. The resources constructed for this research will be made publicly available.
format	article
author	Yan Xu Ji Hua Zhaoheng Ni Qinlang Chen Yubo Fan Sophia Ananiadou Eric I-Chao Chang Junichi Tsujii
author_facet	Yan Xu Ji Hua Zhaoheng Ni Qinlang Chen Yubo Fan Sophia Ananiadou Eric I-Chao Chang Junichi Tsujii
author_sort	Yan Xu
title	Anatomical entity recognition with a hierarchical framework augmented by external resources.
title_short	Anatomical entity recognition with a hierarchical framework augmented by external resources.
title_full	Anatomical entity recognition with a hierarchical framework augmented by external resources.
title_fullStr	Anatomical entity recognition with a hierarchical framework augmented by external resources.
title_full_unstemmed	Anatomical entity recognition with a hierarchical framework augmented by external resources.
title_sort	anatomical entity recognition with a hierarchical framework augmented by external resources.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/975b50d13a40431eb61ed4a8d98c312c
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Anatomical entity recognition with a hierarchical framework augmented by external resources.

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