Medical Entity Relation Extraction Based on Deep Neural Network and Self-attention Mechanism

With the advancement of medical informatization,a large amount of unstructured text data has been accumulated in the medical field.How to mine valuable information from these medical texts is a research hotspot in the field of medical profession and natural language processing.With the development of deep learning,deep neural network is gradually applied to relation extraction task,and "recurrent＋CNN" network framework has become the mainstream model in medical entity relation extraction task.However,due to the problems of high entity density and the cross-connection of relationships between entities in medical texts,the "recurrent＋CNN" network framework cannot deeply mine the semantic features of medical texts.Based on the "recurrent＋CNN" network framework,this paper proposes a Chinese medical entity relation extraction model with multi-channel self-attention mechanism.It includes that BLSTM is used to capture the context information of text sentences,a multi-channel self-attention mechanism is used to mine the global semantic features of sentences,and CNN is used to capture the local phrase features of sentences.The effectiveness of the model is verified by experiments on Chinese medical text dataset.The precision,recall and F1 value of the model are improved compared with the mainstream models.