Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks

Savelie Cornegruta, Robert Bakewell, Samuel Withey, Giovanni Montana
2016 Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis  
Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling radiological language. The model has been used to address two NLP tasks: medical named-entity recognition (NER) and negation detection. We investigate whether learning several types of word embeddings improves BiLSTM's performance on those tasks. Using a large dataset of chest x-ray reports, we
more » ... compare the proposed model to a baseline dictionary-based NER system and a negation detection system that leverages the hand-crafted rules of the NegEx algorithm and the grammatical relations obtained from the Stanford Dependency Parser. Compared to these more traditional rule-based systems, we argue that BiLSTM offers a strong alternative for both our tasks.
doi:10.18653/v1/w16-6103 dblp:conf/acl-louhi/CornegrutaBWM16 fatcat:ub54zeiw6jbc3ec36shvucr65y