Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks
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.
- Publication:
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arXiv e-prints
- Pub Date:
- September 2016
- DOI:
- arXiv:
- arXiv:1609.08409
- Bibcode:
- 2016arXiv160908409C
- Keywords:
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- Computer Science - Computation and Language;
- Statistics - Machine Learning
- E-Print:
- LOUHI 2016 conference proceedings