Recurrent neural networks with specialized word embedding for Chinese Clinical Named Entity Recognition

To extract medical clinical related entity mention from patient clinical records is an essential step in clinical research. Recently, many researchers employ neural architecture to tackle the similar task of clinical concept extraction or drug name recognition from English clinical records, and have got prominent progress. However, most previous systems on Chinese Clinical Named Entity Recognition(CNER) have focused on a combination of text “feature engineering” and conventional machine learning algorithms. In this paper, we proposed a nerual network system based on bidirectional LSTMs and CRF for Chinese CNER. Also, we use health domain datasets to create richer, more specialized word embeddings, and combined with the external health domain lexicons, the performance is futher improved.