Deep Medical Entity Recognition for Swedish and Spanish

Clinical texts, although challenging to process, are rich in valuable information, and named entity recognition is an important element in any system designed to extract relevant information from such texts. Recently, improved performance for named entity recognition has been achieved through deep learning methods, and here, a recurrent neural network is evaluated for medical named entity recognition in clinical texts in two different languages, Spanish and Swedish. An important factor for any machine learning model is the input representation, how the features are preprocessed and presented to the model. Therefore, a number of different embeddings derived from large corpora of clinical texts, and several combination strategies for embeddings have been evaluated for this task. Combining a bidirectional LSTM with embeddings derived from words and lemmas gave an improvement in performance with over three points in average F-measure over using only shallow learning methods for both languages, while at the same time reducing the dependency on external resources and feature engineering, showing this approach to be suitable for medical named entity recognition. An average F-measure of 74.87 is obtained for Spanish using lemma embeddings and of 76.04 for Swedish when concatenated lemma and word embeddings are used.