Medical Named Entity Recognition from Un-labelled Medical Records based on Pre-trained Language Models and Domain Dictionary

Medical named entity recognition (NER) is an area in which medical named entities are recognized from medical texts, such as diseases, drugs, surgery reports, anatomical parts, and examination documents. Conventional medical NER methods do not make full use of un-labelled medical texts embedded in medical documents. To address this issue, we proposed a medical NER approach based on pre-trained language models and a domain dictionary. First, we constructed a medical entity dictionary by extracting medical entities from labelled medical texts and collecting medical entities from other resources, such as the Yidu-N4K data set. Second, we employed this dictionary to train domain-specific pre-trained language models using un-labelled medical texts. Third, we employed a pseudo labelling mechanism in un-labelled medical texts to automatically annotate texts and create pseudo labels. Fourth, the BiLSTM-CRF sequence tagging model was used to fine-tune the pre-trained language models. Our experiments on the un-labelled medical texts, which were extracted from Chinese electronic medical records, show that the proposed NER approach enables the strict and relaxed F1 scores to be 88.7% and 95.3%, respectively.