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SBLC: a hybrid model for disease named entity recognition based on semantic bidirectional LSTMs and conditional random fields.

Background: Disease named entity recognition (NER) is a fundamental step in information processing of medical texts. However, disease NER involves complex issues such as descriptive modifiers in actual practice. The accurate identification of disease NER is a still an open and essential research pro...

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Published in:BMC Medical Informatics & Decision Making Vol. 18; no. 5
Main Authors: Xu, Kai, Liu, Wenyin, Zhou, Zhanfan, Gong, Tao, Hao, Tianyong
Format: Journal Article
Published: BioMed Central 12/7/2018 Supplement 5
Online Access:View this record in EBSCOhost
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      dt: 12/7/2018 Supplement 5
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      pub: BioMed Central
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        10.1186/s12911-018-0690-y
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        atl: SBLC: a hybrid model for disease named entity recognition based on semantic bidirectional LSTMs and conditional random fields.
      aug:
        au:
          Xu, Kai
          Liu, Wenyin
          Zhou, Zhanfan
          Gong, Tao
          Hao, Tianyong
        affil: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
      sug:
        subj:
          Data Mining
          Medical Informatics
          Neural Networks (Computer)
          Semantics
          Barthel Index
          Short Portable Mental Status Questionnaire
      ab: Background: Disease named entity recognition (NER) is a fundamental step in information processing of medical texts. However, disease NER involves complex issues such as descriptive modifiers in actual practice. The accurate identification of disease NER is a still an open and essential research problem in medical information extraction and text mining tasks.Methods: A hybrid model named Semantics Bidirectional LSTM and CRF (SBLC) for disease named entity recognition task is proposed. The model leverages word embeddings, Bidirectional Long Short Term Memory networks and Conditional Random Fields. A publically available NCBI disease dataset is applied to evaluate the model through comparing with nine state-of-the-art baseline methods including cTAKES, MetaMap, DNorm, C-Bi-LSTM-CRF, TaggerOne and DNER.Results: The results show that the SBLC model achieves an F1 score of 0.862 and outperforms the other methods. In addition, the model does not rely on external domain dictionaries, thus it can be more conveniently applied in many aspects of medical text processing.Conclusions: According to performance comparison, the proposed SBLC model achieved the best performance, demonstrating its effectiveness in disease named entity recognition.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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