title: Recognizing Disjoint Clinical Concepts in Clinical Text Using Machine Learning-based Methods.
abstract: Clinical concept recognition (CCR) is a fundamental task in clinical natural language processing (NLP) field. Almost all current machine learning-based CCR systems can only recognize clinical concepts of consecutive words (called consecutive clinical concepts, CCCs), but can do nothing about clinical concepts of disjoint words (called disjoint clinical concepts, DCCs), which widely exist in clinical text. In this paper, we proposed two novel types of representations for disjoint clinical concepts, and applied two state-of-the-art machine learning methods to recognizing consecutive and disjoint concepts. Experiments conducted on the 2013 ShARe/CLEF challenge corpus showed that our best system achieved a "strict" F-measure of 0.803 for CCCs, a "strict" F-measure of 0.477 for DCCs, and a "strict" F-measure of 0.783 for all clinical concepts, significantly higher than the baseline systems by 4.2% and 4.1% respectively.
authors.affiliation: Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China; School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
authors.forename: Buzhou
authors.initials: B
authors.lastname: Tang
authors.affiliation: Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.
authors.forename: Qingcai
authors.initials: Q
authors.lastname: Chen
authors.affiliation: Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.
authors.forename: Xiaolong
authors.initials: X
authors.lastname: Wang
authors.affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
authors.forename: Yonghui
authors.initials: Y
authors.lastname: Wu
authors.affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
authors.forename: Yaoyun
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
authors.forename: Min
authors.initials: M
authors.lastname: Jiang
authors.affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
authors.forename: Jingqi
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
authors.forename: Hua
authors.initials: H
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D006801:Humans; D000069550:Machine Learning; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D012660:Semantics