title: A comprehensive study of named entity recognition in Chinese clinical text.
abstract: OBJECTIVE
  Named entity recognition (NER) is one of the fundamental tasks in natural language processing. In the medical domain, there have been a number of studies on NER in English clinical notes; however, very limited NER research has been carried out on clinical notes written in Chinese. The goal of this study was to systematically investigate features and machine learning algorithms for NER in Chinese clinical text.
  
  
  METHODS
  We randomly selected 400 admission notes and 400 discharge summaries from Peking Union Medical College Hospital in China. For each note, four types of entity-clinical problems, procedures, laboratory test, and medications-were annotated according to a predefined guideline. Two-thirds of the 400 notes were used to train the NER systems and one-third for testing. We investigated the effects of different types of feature including bag-of-characters, word segmentation, part-of-speech, and section information, and different machine learning algorithms including conditional random fields (CRF), support vector machines (SVM), maximum entropy (ME), and structural SVM (SSVM) on the Chinese clinical NER task. All classifiers were trained on the training dataset and evaluated on the test set, and micro-averaged precision, recall, and F-measure were reported.
  
  
  RESULTS
  Our evaluation on the independent test set showed that most types of feature were beneficial to Chinese NER systems, although the improvements were limited. The system achieved the highest performance by combining word segmentation and section information, indicating that these two types of feature complement each other. When the same types of optimized feature were used, CRF and SSVM outperformed SVM and ME. More specifically, SSVM achieved the highest performance of the four algorithms, with F-measures of 93.51% and 90.01% for admission notes and discharge summaries, respectively.
authors.affiliation: Center for Medical Informatics, Peking University, Beijing, China The University of Texas School of Biomedical Informatics at Houston, Houston, Texas, USA.
authors.forename: Jianbo
authors.initials: J
authors.lastname: Lei
authors.affiliation: The University of Texas School of Biomedical Informatics at Houston, Houston, Texas, USA Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.
authors.forename: Buzhou
authors.initials: B
authors.lastname: Tang
authors.affiliation: Center for Medical Informatics, Peking University, Beijing, China.
authors.forename: Xueqin
authors.initials: X
authors.lastname: Lu
authors.affiliation: Center for Medical Informatics, Peking University, Beijing, China.
authors.forename: Kaihua
authors.initials: K
authors.lastname: Gao
authors.affiliation: The University of Texas School of Biomedical Informatics at Houston, Houston, Texas, USA.
authors.forename: Min
authors.initials: M
authors.lastname: Jiang
authors.affiliation: The University of Texas School of Biomedical Informatics at Houston, Houston, Texas, USA.
authors.forename: Hua
authors.identifier: http://orcid.org/0000-0001-9730-7276
authors.initials: H
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002681:China; D057286:Electronic Health Records; D006801:Humans; D009323:Natural Language Processing; D010343:Patient Admission; D060388:Support Vector Machine
keywords: Chinese clinic notes; Machine learning algorithm; Medical concept recognition; Natural language processing