With the rapid development of biomedical research and information technology, the number of clinical medical literature has increased exponentially. At present, COVID‐19 clinical text research has some problems, such as lack of corpus and poor annotation quality. In clinical medical literature, there are many medical related semantic relationships between entities. After the task of entity recognition, how to further extract the relationships between entities efficiently and accurately becomes very critical. In this study, a COVID‐19 clinical trial data relationship extraction model based on deep learning method is proposed. The model adopts MPNet model, bidirectional‐GRU (BiGRU) network, MAtt mechanism and Conditional Random Field inference layer integration architecture and improves the problem that static word vector cannot represent ambiguity through pre‐trained language model. BiGRU network is used to replace the current Bi directional long short term memory structure and simplify the network structure of Long Short Term Memory to improve the training efficiency of the model. Through comparative experiments, the proposed method performs well in the COVID‐19 clinical text entity relation extraction task.
With the continuous breakthroughs in artificial intelligence technology, it has become easier to extract general-purpose knowledge using machine learning, but it is a challenging task to extract and learn small samples of knowledge in medical expertise. On the one hand, it is difficult to represent medical expertise entities, and on the other hand, the training samples of such expertise are small, and deep learning methods often require a large number of samples to complete the learning task. To this end, we proposes a graph network learning method for specialized vocabulary representation. Specifically, a contextual knowledge representation model based on graph meta-learning is proposed, which combines text, phrase, vocabulary, and other information to solve the problem of sparse data of medical electronic medical record entities that cannot be extracted and learned. In this method, a text-independent lexical representation learning method, a context-aware graph neural network, and a combined LSTM language model are used to model information from different perspectives as a way to learn semantic representations of professional discourse entities. The experimental results show that the accuracy of the method outperforms other similar methods and proves its effectiveness.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.