title: Anatomical entity recognition with a hierarchical framework augmented by external resources.
abstract: 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.
authors.affiliation: State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China; Microsoft Research Asia, Beijing, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Xu
authors.affiliation: State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China.
authors.forename: Ji
authors.initials: J
authors.lastname: Hua
authors.affiliation: State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China.
authors.forename: Zhaoheng
authors.initials: Z
authors.lastname: Ni
authors.affiliation: State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China.
authors.forename: Qinlang
authors.initials: Q
authors.lastname: Chen
authors.affiliation: State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China.
authors.forename: Yubo
authors.initials: Y
authors.lastname: Fan
authors.affiliation: The National Centre for Text Mining, School of Computer Science, The University of Manchester, Manchester, United Kingdom.
authors.forename: Sophia
authors.initials: S
authors.lastname: Ananiadou
authors.affiliation: Microsoft Research Asia, Beijing, China.
authors.forename: Eric I-Chao
authors.initials: EI
authors.lastname: Chang
authors.affiliation: Microsoft Research Asia, Beijing, China.
authors.forename: Junichi
authors.initials: J
authors.lastname: Tsujii
mesh_terms: D000465:Algorithms; D000715:Anatomy; D001185:Artificial Intelligence; D006801:Humans; D051188:Knowledge Bases; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D012660:Semantics; D018875:Vocabulary, Controlled