Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition.

•Past approaches to health-domain NER have mainly used manual features and conventional classifiers.•In this paper, we explore a neural network approach (B-LSTM-CRF) that can learn the features automatically.•In addition, initializing the features with pre-trained embeddings can lead to higher accuracy.•We pre-train the features using a critical care database (MIMIC-III).•Experiments have been carried out over three contemporary datasets for health-domain NER, outperforming past systems.