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    StaResGRU-CNN with CMedLMs: a stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence

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    Authors
    Ni, Pin
    Li, Gangmin
    Hung, Patrick C.K.
    Chang, Victor
    Affiliation
    University College London
    University of Bedfordshire
    Ontario Tech University
    Teesside University
    Issue Date
    2021-10-13
    Subjects
    named entity recognition
    pre-trained language model
    text classification
    transfer learning
    biomedical text mining
    predictive intelligence
    natural language processing
    
    Metadata
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    Abstract
    As a task requiring strong professional experience as supports, predictive biomedical intelligence cannot be separated from the support of a large amount of external domain knowledge. By using transfer learning to obtain sufficient prior experience from massive biomedical text data, it is essential to promote the performance of specific downstream predictive and decision-making task models. This is an efficient and convenient method, but it has not been fully developed for Chinese Natural Language Processing (NLP) in the biomedical field. This study proposes a Stacked Residual Gated Recurrent Unit-Convolutional Neural Networks (StaResGRU-CNN) combined with the pre-trained language models (PLMs) for biomedical text-based predictive tasks. Exploring related paradigms in biomedical NLP based on transfer learning of external expert knowledge and comparing some Chinese and English language models. We have identified some key issues that have not been developed or those present difficulties of application in the field of Chinese biomedicine. Therefore, we also propose a series of Chinese bioMedical Language Models (CMedLMs) with detailed evaluations of downstream tasks. By using transfer learning, language models are introduced with prior knowledge to improve the performance of downstream tasks and solve specific predictive NLP tasks related to the Chinese biomedical field to serve the predictive medical system better. Additionally, a free-form text Electronic Medical Record (EMR)-based Disease Diagnosis Prediction task is proposed, which is used in the evaluation of the analyzed language models together with Clinical Named Entity Recognition, Biomedical Text Classification tasks. Our experiments prove that the introduction of biomedical knowledge in the analyzed models significantly improves their performance in the predictive biomedical NLP tasks with different granularity. And our proposed model also achieved competitive performance in these predictive intelligence tasks.
    Citation
    Ni P, Li G, Hung PCK, Chang V (2021) 'StaResGRU-CNN with CMedLMs: a stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence', Applied Soft Computing, 113 B (107975)
    Publisher
    Elsevier Ltd
    Journal
    Applied Soft Computing
    URI
    http://hdl.handle.net/10547/625294
    DOI
    10.1016/j.asoc.2021.107975
    Additional Links
    https://www.sciencedirect.com/science/article/abs/pii/S1568494621008978
    Type
    Article
    Language
    en
    ISSN
    1568-4946
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.asoc.2021.107975
    Scopus Count
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    Computing

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