A Chinese Named Entity Recognition System with Neural Networks

Yi, Huikang; Huang, Jiuming; Yang, Shuqiang

doi:10.1051/itmconf/20171204002

Cited by 4 publications

(1 citation statement)

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“…Various ML models have shown significant performance on CNER on English EHRs. Compared with English CNER, Chinese CNER faces more obstacles and still remains a challenge, which may due to the following reasons: (1) few open access Chinese EHR corpora; (2) a small number of Chinese medical dictionaries and ontology libraries; and (3) complicated properties of the Chinese language, such as the lack of word boundaries, the complex composition forms, and word forms remaining unchanged in all kinds of tense or POS [18,19]. Until the recent 2 years, the number of studies on Chinese CNER has increased rapidly, boosting the performance of the models on Chinese CNER.…”

Section: Introductionmentioning

confidence: 99%

Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods

Zhang¹,

Wang²,

Hou³

et al. 2018

JMIR Med Inform

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BackgroundElectronic health records (EHRs) are important data resources for clinical studies and applications. Physicians or clinicians describe patients’ disorders or treatment procedures in EHRs using free text (unstructured) clinical notes. The narrative information plays an important role in patient treatment and clinical research. However, it is challenging to make machines understand the clinical narratives.ObjectiveThis study aimed to automatically identify Chinese clinical entities from free text in EHRs and make machines semantically understand diagnoses, tests, body parts, symptoms, treatments, and so on.MethodsThe dataset we used for this study is the benchmark dataset with human annotated Chinese EHRs, released by the China Conference on Knowledge Graph and Semantic Computing 2017 clinical named entity recognition challenge task. Overall, 2 machine learning models, the conditional random fields (CRF) method and bidirectional long short-term memory (LSTM)-CRF, were applied to recognize clinical entities from Chinese EHR data. To train the CRF–based model, we selected features such as bag of Chinese characters, part-of-speech tags, character types, and the position of characters. For the bidirectional LSTM-CRF–based model, character embeddings and segmentation information were used as features. In addition, we also employed a dictionary-based approach as the baseline for the purpose of performance evaluation. Precision, recall, and the harmonic average of precision and recall (F1 score) were used to evaluate the performance of the methods.ResultsExperiments on the test set showed that our methods were able to automatically identify types of Chinese clinical entities such as diagnosis, test, symptom, body part, and treatment simultaneously. With regard to overall performance, CRF and bidirectional LSTM-CRF achieved a precision of 0.9203 and 0.9112, recall of 0.8709 and 0.8974, and F1 score of 0.8949 and 0.9043, respectively. The results also indicated that our methods performed well in recognizing each type of clinical entity, in which the “symptom” type achieved the best F1 score of over 0.96. Moreover, as the number of features increased, the F1 score of the CRF model increased from 0.8547 to 0.8949.ConclusionsIn this study, we employed two computational methods to simultaneously identify types of Chinese clinical entities from free text in EHRs. With training, these methods can effectively identify various types of clinical entities (eg, symptom and treatment) with high accuracy. The deep learning model, bidirectional LSTM-CRF, can achieve better performance than the CRF model with little feature engineering. This study contributed to translating human-readable health information into machine-readable information.

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Section: Introductionmentioning

confidence: 99%