BertMCN: Mapping colloquial phrases to standard medical concepts using BERT and highway network

In the last few years, people started to share lots of information related to health in the form of tweets, reviews and blog posts. All these user generated clinical texts can be mined to generate useful insights. However, automatic analysis of clinical text requires identification of standard medical concepts. Most of the existing deep learning based medical concept normalization systems are based on CNN or RNN. Performance of these models is limited as they have to be trained from scratch (except embeddings). In this work, we propose a medical concept normalization system based on BERT and highway layer. BERT, a pre-trained context sensitive deep language representation model advanced state-of-the-art performance in many NLP tasks and gating mechanism in highway layer helps the model to choose only important information. Experimental results show that our model outperformed all existing methods on two standard datasets. Further, we conduct a series of experiments to study the impact of different learning rates and batch sizes, noise and freezing encoder layers on our model.

Automated summary

In this study, a medical concept normalization system based on BERT and highway layer is proposed, and experimental results show that it outperforms existing methods on two standard datasets. Furthermore, the impact of different learning rates and batch sizes, noise, and freezing encoder layers on the model was also investigated through a series of experiments.