Medical Named Entity Recognition Incorporating Word Information and Graph Attention.

The Chinese clinical natural language is rich in a large amount of medical record information. Naming entity recognition for electronic medical records can help establish medical auxiliary diagnostic systems, which is of great significance for the development of the medical field. At the same time, it is conducive to downstream tasks such as relationship extraction and the implementation of knowledge graphs. However, Chinese electronic medical records have problems with difficulty in Chinese word segmentation, numerous medical terminology, and special expressions, which can easily lead to incorrect expression of text features. Therefore, this paper proposes a medical named entity recognition research model based on enhanced word information and graph attention, which improves the performance of the network model by enhancing local and global features. Due to the fact that embedding a single word vector for Chinese entity recognition can easily ignore word information and semantics in the text, this paper embeds a highly correlated word vector in the word vector, which not only enhances text representation but also avoids word segmentation errors. Additionally, a Med- Bert model for learning medical knowledge is embedded in the word embedding layer, which can dynamically generate feature vectors according to different contexts, helps solve the problem of polysemy and specialized vocabulary in electronic medical records. At the same time, adding a graph attention module in the coding layer enhances the network's ability to learn text context relationships and enhances the model's learning of medical special grammar. Finally, F1 values of 86.38% and 84.76% are obtained on the cEHRNER and cMedQANER datasets, respectively, showing better robustness compared to other models. [ABSTRACT FROM AUTHOR]