Abstract
Named entity recognition is the first step in clinical electronic medical record text mining, which is significant for clinical decision support and personalized medicine. However, the lack of annotated electronic medical record datasets limits the application of pre-trained language models and deep neural networks in this field. To alleviate the problem of data scarcity, we propose T-RoBERTa-BiLSTM-CRF, a transfer learning-based electronic medical record entity recognition model, which aggregates the characteristics of medical data from different sources and uses a small amount of electronic medical record data as target data for further training. Compared with existing models, our approach can model medical entities more effectively, and the extensive comparative experiments on the CCKS 2019 and DEMRC datasets show the effectiveness of our approach.
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