A review of international clinical trial registration

Yu, He; Liu, Jianping

doi:10.3736/jcim20070302

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…Calculate the forgetting gate according to formula (1), where the input is the hidden state h t−1 at the previous moment and the input word X t at the current moment.…”

Section: Long Short-term Memorymentioning

confidence: 99%

“…After BiGRU feature extraction and MAtt mechanism weight operation, some constraint rules are automatically added to the final predicted label (the output result of the BiGRU layer) through the CRF layer: (1)The first word of the entity should start with "B-" or "O"; (2)Valid patterns should be "O B-[Label]" or "B-[Label] I-[Label]"; the "O I-[Label]" mode is regarded as the invalid mode. Finally, in the decoding stage, the Veterbi algorithm is used to obtain the label sequence with the highest predicted total score in the sequence, and use it as the entity relationship classification result of the COVID-19 clinical trial registration paper.…”

Section: Design Of Entity Relation Extraction Model For Clinical Trialsmentioning

confidence: 99%

“…In the field of medical research, clinical trials are one of the important means to promote the development of human health [1]. Since 2019, Corona Virus disease 2019 (COVID- 19), an acute respiratory infectious disease caused by SARS coronavirus type 2, has become an unprecedented public health crisis, posing a serious threat to human life safety [2,3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

COVID‐19 clinical medical relationship extraction based on MPNet

Pan

Cai

et al. 2023

IET Cyber-Phy Sys Theory & Ap

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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.

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“…Calculate the forgetting gate according to formula (1), where the input is the hidden state h t−1 at the previous moment and the input word X t at the current moment.…”

Section: Long Short-term Memorymentioning

confidence: 99%

Section: Design Of Entity Relation Extraction Model For Clinical Trialsmentioning

confidence: 99%