Medical named entity recognition is a fundamental and essential research for medical natural language possessing, aiming to identifying medical concepts or terminology such as diseases, drugs, treatments, procedures, etc. from unstructured medical text. A model based on a bidirectional LSTM and conditional random fields (Bi-LSTM-CRF) is proposed for medical named entity recognition. Our model contains three layers and relies on character-based word representations learned from the supervised corpus. BiLSTM-CRF model can learn the information characteristics of a given dataset. Experiments on a publi-cally available NCBI Disease Corpus as an evaluation standard dataset shows our approach achieves a 0.8022 F1 measure, which outperforms a number of widely used baseline methods.
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