Medical Relationship Classification Method Based on Dual Channel Attention DOI
Ziqi Zhang, Xiangwei Zheng, Jinsong Zhang

et al.

Published: Nov. 24, 2023

Electronic medical record mining based on relationship classification has become a hot topic in the field of healthcare. However, existing models classification, most them use single-layer attention, it results relatively simple feature representation and is easy to lose information during training. Therefore, this paper proposes method dual channel attention. Firstly, 1 combines BERT(Bidirectional Encoder Representation from Transformers), GRU(Gate Recurrent Unit) Global Attention, while 2 Subject_object_mask_generation So Attention. Specifically, we module specify corresponding positions subject object within text. And Attention used focus attention between object. Secondly, outputs two channels are concatenated. Finally, perform concatenated results. We evaluated public dataset CMeIE(Chinese Medical Information Extraction), experimental showed that improved model's accuracy, recall F1 values increased by 2.2%, 0.03% 1.3% respectively, compared baseline. It indicates our certain advantages other methods.

Language: Английский

Medical Relationship Classification Method Based on Dual Channel Attention DOI
Ziqi Zhang, Xiangwei Zheng, Jinsong Zhang

et al.

Published: Nov. 24, 2023

Electronic medical record mining based on relationship classification has become a hot topic in the field of healthcare. However, existing models classification, most them use single-layer attention, it results relatively simple feature representation and is easy to lose information during training. Therefore, this paper proposes method dual channel attention. Firstly, 1 combines BERT(Bidirectional Encoder Representation from Transformers), GRU(Gate Recurrent Unit) Global Attention, while 2 Subject_object_mask_generation So Attention. Specifically, we module specify corresponding positions subject object within text. And Attention used focus attention between object. Secondly, outputs two channels are concatenated. Finally, perform concatenated results. We evaluated public dataset CMeIE(Chinese Medical Information Extraction), experimental showed that improved model's accuracy, recall F1 values increased by 2.2%, 0.03% 1.3% respectively, compared baseline. It indicates our certain advantages other methods.

Language: Английский

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