StaResGRU-CNN with CMedLMs: a stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence
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Affiliation
University College LondonUniversity of Bedfordshire
Ontario Tech University
Teesside University
Issue Date
2021-10-13Subjects
named entity recognitionpre-trained language model
text classification
transfer learning
biomedical text mining
predictive intelligence
natural language processing
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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 LtdJournal
Applied Soft ComputingType
ArticleLanguage
enISSN
1568-4946ae974a485f413a2113503eed53cd6c53
10.1016/j.asoc.2021.107975