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Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks

2016-09-27 13:25:10
Savelie Cornegruta, Robert Bakewell, Samuel Withey, Giovanni Montana
arXiv_CV
                   
Abstract

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 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.

Abstract (translated)

URL

https://arxiv.org/abs/1609.08409

PDF

https://arxiv.org/pdf/1609.08409.pdf

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