Character-level neural network model based on Nadam optimization and its application in clinical concept extraction

Clinical concept extraction aims to quickly and effectively extract available data from complex and diverse clinical information, which is a crucial task for medical diagnosis using electronic medical records. Named entity recognition (NER) accurately marks essential information in clinical records based on the characteristics of the target entity, providing a way to extract clinical concepts. In the clinical concept extraction task, the existing methods are not satisfactory to obtain accurate labelling results in the face of large-scale and complex clinical information. To solve this problem, we improve and optimize a named entity recognition method based on the LSTM-CRF model. First, the improved deep neural network model uses two optional configurations of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BLSTM) to achieve character-level representation. Then the BLSTM layer obtains the context information of the target word, and the Conditional Random Field (CRF) gives constraints to ensure the standardization of the label. On this basis, Nadam is used to optimize the training process of the network. The experimental results show that our new method has an F1 score of 84.61 on the public dataset of 2010 i2b2/VA concept extraction task, which exceeds the LSTM-CRF model. And the recall of 85.41 is ahead of all the methods evaluated on this data set.