A multiclass classification method based on deep learning for named entity recognition in electronic medical records

Research of named entity recognition (NER) on electrical medical records (EMRs) focuses on verifying whether methods to NER in traditional texts are effective for that in EMRs, and there is no model proposed for enhancing performance of NER via deep learning from the perspective of multiclass classification. In this paper, we annotate a real EMR corpus to accomplish the model training and evaluation. And, then, we present a Convolutional Neural Network (CNN) based multiclass classification method for mining named entities from EMRs. The method consists of two phases. In the phase 1, EMRs are pre-processed for representing samples with word embedding. In the phase 2, the method is built by segmenting training data into many subsets and training a CNN binary classification model on each of subset. Experimental results showed the effectiveness of our method.