J Am Med Inform Assoc - A comprehensive study of named entity recognition in Chinese clinical text.

Tópicos

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Resumo

JECTIVE: 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.MATERIALS AND 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.

Resumo Limpo

jectiv name entiti recognit ner one fundament task natur languag process medic domain number studi ner english clinic note howev limit ner research carri clinic note written chines goal studi systemat investig featur machin learn algorithm ner chines clinic textmateri method random select admiss note discharg summari peke union medic colleg hospit china note four type entityclin problem procedur laboratori test medicationswer annot accord predefin guidelin twothird note use train ner system onethird test investig effect differ type featur includ bagofcharact word segment partofspeech section inform differ machin learn algorithm includ condit random field crf support vector machin svm maximum entropi structur svm ssvm chines clinic ner task classifi train train dataset evalu test set microaverag precis recal fmeasur reportedresult evalu independ test set show type featur benefici chines ner system although improv limit system achiev highest perform combin word segment section inform indic two type featur complement type optim featur use crf ssvm outperform svm specif ssvm achiev highest perform four algorithm fmeasur admiss note discharg summari respect

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