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Relation Extraction from Clinical Narratives Using Pre-trained Language Models

AMIA ... Annual Symposium proceedings, 2019, Vol.2019, p.1236-1245 [Peer Reviewed Journal]

2019 AMIA - All rights reserved. ;2019 AMIA - All rights reserved. 2019 ;EISSN: 1942-597X ;PMID: 32308921

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  • Title:
    Relation Extraction from Clinical Narratives Using Pre-trained Language Models
  • Author: Wei, Qiang ; Ji, Zongcheng ; Si, Yuqi ; Du, Jingcheng ; Wang, Jingqi ; Tiryaki, Firat ; Wu, Stephen ; Tao, Cui ; Roberts, Kirk ; Xu, Hua
  • Subjects: Datasets as Topic ; Humans ; Information Storage and Retrieval - methods ; Machine Learning ; Narration ; Natural Language Processing ; Semantics
  • Is Part Of: AMIA ... Annual Symposium proceedings, 2019, Vol.2019, p.1236-1245
  • Description: Natural language processing (NLP) is useful for extracting information from clinical narratives, and both traditional machine learning methods and more-recent deep learning methods have been successful in various clinical NLP tasks. These methods often depend on traditional word embeddings that are outputs of language models (LMs). Recently, methods that are directly based on pre-trained language models themselves, followed by fine-tuning on the LMs (e.g. the Bidirectional Encoder Representations from Transformers (BERT)), have achieved state-of-the-art performance on many NLP tasks. Despite their success in the open domain and biomedical literature, these pre-trained LMs have not yet been applied to the clinical relation extraction (RE) task. In this study, we developed two different implementations of the BERT model for clinical RE tasks. Our results show that our tuned LMs outperformed previous state-of-the-art RE systems in two shared tasks, which demonstrates the potential of LM-based methods on the RE task.
  • Publisher: United States: American Medical Informatics Association
  • Language: English
  • Identifier: EISSN: 1942-597X
    PMID: 32308921
  • Source: PubMed
    GFMER Free Medical Journals
    MEDLINE
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