Projects per year
We have developed the rule-based EdIE-R-Neg, part of an existing text mining pipeline called EdIE-R (Edinburgh Information Extraction for Radiology reports), developed to process brain imaging reports, 1and two machine learning approaches; one using a bidirectional long short-term memory network and another using a feedforward neural network. These were developed on data from the Edinburgh Stroke Study, and tested on data from routine reports from NHS Tayside (Tayside). Both datasets consist of written reports from medical scans.
These models are compared with two existing rule-based models; pyConText [Harkema et al., 2009], a python implementation of a generalisation of NegEx, and NegBio [Penget al., 2017], which identifies negation scopes through patterns applied to a syntactic representation of the sentence. On both the test set of the dataset from which our models were developed, as well as the largely similar Tayside test set, the neural network models and our custom-built rule-based system outperformed the existing methods.
EdIE-R-Neg scored highest on F1 score, particularly on the test set of the Tayside dataset, from which no development data was used in these experiments, showing the power of custom-built rule-based systems for negation detection on datasets of this size.
The performance gap of the machine learning models to EdIE-R-Neg on the Tayside test set was reduced through adding development Tayside data into the ESS training set, demonstrating the adaptability of the neural network models
- Machine Learning
- Natural Language Processing for Biomedical Texts
- Corpus annotation
- information exploitation
- Text Data Mining
FingerprintDive into the research topics of 'Comparison of Rule-based and Neural Network Models for Negation Detection in Radiology Reports'. Together they form a unique fingerprint.
- 6 Finished
1/11/18 → 31/08/21
1/11/18 → 31/08/21