Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks
This work addresses the need for efficient information extraction from free-text radiological reports, which is incremental as it applies an existing neural method to a specific medical domain.
The authors tackled the problem of automating medical information extraction from radiological reports by applying a bidirectional long short-term memory (BiLSTM) network to named-entity recognition and negation detection, showing it offers a strong alternative to traditional rule-based systems.
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.