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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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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. ; over 3m from acceptance/publication
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Keyword:
biomedical text mining; named entity recognition; natural language processing; pre-trained language model; predictive intelligence; text classification; transfer learning
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URL: http://hdl.handle.net/10547/625294
https://doi.org/10.1016/j.asoc.2021.107975
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Machine Learning approaches for Topic and Sentiment Analysis in multilingual opinions and low-resource languages: From English to Guarani
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Brazilian Portuguese verbal databases ; Bases lexicais verbais do português brasileiro
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In: Domínios de Lingu@gem; Ahead of Print ; 1980-5799 (2022)
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Representation learning of natural language and its application to language understanding and generation
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Detecting weak and strong Islamophobic hate speech on social media
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An Empirical Study of Factors Affecting Language-Independent Models
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Using Geolocated Text to Quantify Location in Real Estate Appraisal
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TSM: Measuring the Enticement of Honeyfiles with Natural Language Processing
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Modeling Phishing Decision using Instance Based Learning and Natural Language Processing
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What to prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development
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Eine agentenbasierte Architektur für Programmierung mit gesprochener Sprache
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Um método adaptativo para análise sintática do Português Brasileiro. ; An adaptive method for syntactic analysis of Brazilian Portuguese.
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Padovani, Djalma. - : Biblioteca Digital de Teses e Dissertações da USP, 2022. : Universidade de São Paulo, 2022. : Escola Politécnica, 2022
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Multitask Pointer Network for Multi-Representational Parsing
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