The system Edinburgh Information Extraction for Radiology reports (EdIE-R) is a rule-based text mining pipeline for classifying radiologists’ reports of CT and MRI brain scans, assigning labels indicating occurrence and type of stroke, as well as other observations. EdIE-R identifies entities, negation and relations between entities in each report and determines report-level labels.
EdIE-R was developed based on extensive analysis of Scottish brain imaging reports and their manually enriched annotations created by domain experts. The datasets that were annotated for system development and evaluation are the Edinburgh Stroke Study and brain imaging reports from NHS Tayside. EdIE-R was also run over a small set of radiology reports obtained from the Salford Royal NHS with the aim to extend the analysis to a larger set.
EdIE-R is currently being prepared for an official release to the research community.
EdIE-R has been developed as part of work funded by Dr. Claire Grover’s and Dr. Beatrice Alex’s Turing Fellowships from The Alan Turing Institute (EPSRC grant EP/N510129/1), Dr. William Whiteley’s MRC Clinician Scientist Award (G0902303) and his Scottish Senior Clinical Fellowship (CAF/17/01).
To be added
- Beatrice Alex, Claire Grover, Richard Tobin, Cathie Sudlow, Grant Mair and William Whiteley, Text Mining Brain Imaging Reports, accepted for a special issue to appear in the Journal of Biomedical Semantics in 2019. [preprint]
- Claire Grover, Richard Tobin, Beatrice Alex, Catherine Sudlow, Grant Mair and William Whiteley (2018). Text Mining Brain Imaging Reports, HealTAC-2018, Manchester, UK.
- William Whiteley, Claire Grover, Beatrice Alex, Cathie Sudlow and Grant Mair (2016). A natural language processing algorithm to identify stroke in brain imaging reports on a large scale. Poster presented at the 2nd European Stroke Organisation Conference (ESOC 2016), Barcelona, Spain. [pdf]