Research on Knowledge Graph-based Medical Q&A Methods

In order to enable users to conveniently query medical knowledge online and promote the construction of smart healthcare, a medical Chinese named entity recognition model based on RoBERTa-BiSRU-CRF is designed. Firstly, the model uses RoBERTa to extract feature word vectors of medical data; secondly, BiSRU model is used to extract high-dimensional global sequence features of medical text; finally, conditional random field is used to output the global optimal label sequence. Finally, the tag sequence results are sent to the Medical Knowledge Graph to query the answers. Validated by experiments on the CMeEE medical dataset. The experimental results show that the proposed RoBERTa-BiSRU-CRF entity recognition model achieves an accuracy of 95.16%, which is significantly better than the other models in the experiments, proving the effectiveness of the model.