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

  • Pin Ni
    ,
  • Gangmin Li
    ,
  • Patrick C.K. Hung
    ,
  • Victor Chang
  • University College London
    ,
  • Ontario Tech University
    ,
  • Teesside University
Research Output: Contribution to journal Article Peer-review

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.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Article number

107975

Journal (Volume, Issue Number)

Applied Soft Computing (Volume 113 B, Issue 107975)

Publication milestones

  • Accepted/In press - 02/10/2021
  • Published - 13/10/2021

Publication status

Published - 13/10/2021

ISSN

1568-4946

External Publication IDs

  • handle.net: 10547/625294
  • Scopus: 85118700862

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