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Anatomical entity recognition with a hierarchical framework augmented by external resources

Xu, Yan ; Hua, Ji ; Ni, Zhaoheng ; Chen, Qinlang ; Fan, Yubo ; Ananiadou, Sophia ; Chang, Eric I-Chao ; Tsujii, Junichi Homayouni, Ramin

PloS one, 2014-10, Vol.9 (10), p.e108396-e108396 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Anatomical entity recognition with a hierarchical framework augmented by external resources
Autor: Xu, Yan ; Hua, Ji ; Ni, Zhaoheng ; Chen, Qinlang ; Fan, Yubo ; Ananiadou, Sophia ; Chang, Eric I-Chao ; Tsujii, Junichi
Homayouni, Ramin
Assuntos: Abbreviations ; Algorithms ; Anatomy ; Artificial Intelligence ; Automation ; Bioinformatics ; Biomechanics ; Computational linguistics ; Computer and Information Sciences ; Dictionaries ; Education ; Experts ; Finite element method ; Gene expression ; Genomics ; Humans ; Informatics ; Knowledge ; Knowledge base ; Knowledge Bases ; Laboratories ; Language ; Language processing ; Medical records ; Medical treatment ; Natural language interfaces ; Natural Language Processing ; Ontology ; Pattern Recognition, Automated ; Recall ; Recognition ; Semantics ; Vocabulary, Controlled
É parte de: PloS one, 2014-10, Vol.9 (10), p.e108396-e108396
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Conceived and designed the experiments: YX JH ZN YF QC SA JT EC. Performed the experiments: JH ZN QC. Analyzed the data: YX JH ZN. Wrote the paper: YX JH ZN QC YF SA JT EC. Designed the program for analysis: YX QC.
Competing Interests: Funding was received from Microsoft Research Asia, but is not accompanied by any other relevant declarations relating to employment, consultancy, patents, products in development or marketed products and does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.
Descrição: 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.
Editor: United States: Public Library of Science
Idioma: Inglês

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Anatomical entity recognition with a hierarchical framework augmented by external resources

Xu, Yan ; Hua, Ji ; Ni, Zhaoheng ; Chen, Qinlang ; Fan, Yubo ; Ananiadou, Sophia ; Chang, Eric I-Chao ; Tsujii, Junichi Homayouni, Ramin

United States: Public Library of Science

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