The Edinburgh Information Extraction for Radiology reports (EdIE-R) system is a rule-based text mining pipeline designed to classify radiologists’ reports of CT and MRI brain scans. It automatically assigns phenotypes, labels that indicate the presence and type of conditions such as stroke and tumours, along with other key observations. EdIE-R extracts entities, detects negation, identifies relationships between entities within each report, and generates report-level labels.
Developed through extensive analysis and validation using Scottish brain imaging reports and expert-annotated data, EdIE-R was evaluated on datasets from the Edinburgh Stroke Study and NHS Tayside. The system has also been tested on radiology reports from Salford Royal NHS and validated using reports from Generation Scotland.
Most recently, EdIE-R has been applied to 1.7 million Scottish brain imaging reports available through the Scottish National Safe Haven. Its output is being used in conjunction with imaging and linked data to predict dementia and other diseases.
EdIE-R is publicly available for further research and development. It was created through funding from Dr. Claire Grover’s and Dr. Beatrice Alex’s Turing Fellowships at The Alan Turing Institute (EPSRC grant EP/N510129/1) and Dr. William Whiteley’s MRC Clinician Scientist Award (G0902303) and Scottish Senior Clinical Fellowship (CAF/17/01).
More information on how EdIE-R has been developed and applied in different projects can be found here.
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A version of EdIE-R for which we published performance in Alex et al. (2019) can be downloaded here.
Publications
- Arlene Casey, Emma Davidson, Claire Grover, Richard Tobin, Andreas Grivas, Huayu Zhang, Patrick Schrempf, Alison Q. O’Neil, Liam Lee, Michael Walsh, Freya Pellie, 2023). Understanding the performance and reliability of NLP tools: a comparison of four NLP tools predicting stroke phenotypes in radiology reports. Frontiers in digital health, 5, p.1184919. [pdf, DOI]
- Emma M. Davidson, Michael T.C. Poon, Arlene Casey, Andreas Grivas, Daniel Duma, Hang Dong, Víctor Suárez-Paniagua, Claire Grover, Richard Tobin, Heather Whalley, Honghan Wu, Beatrice Alex and William Whiteley (2021). The reporting quality of natural language processing studies: systematic review of studies of radiology reports. BMC Medical Imaging, 21, 142. [pdf, DOI]
- Arlene Casey, Emma Davidson, Michael Poon, Hang Dong, Daniel Duma, Andreas Grivas, Claire Grover, Víctor Suárez-Paniagua, Richard Tobin, William Whiteley, Honghan Wu and Beatrice Alex (2021). A Systematic Review of Natural Language Processing Applied to Radiology Reports. BMC Medical Informatics and Decision Making, 21, 179. [arXiv, pdf, DOI]
- Andreas Grivas, Beatrice Alex, Claire Grover, Richard Tobin, William Whiteley (2020). Not a cute stroke: Analysis of Rule- and Neural Network-Based Information Extraction Systems for Brain Radiology Reports, in Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis (LOUHI 2020) at EMNLP 2020, November 2020. [pdf]
- Dominic Sykes, Andreas Grivas, Claire Grover, Richard Tobin, Cathie Sudlow, William Whiteley, Andrew McIntosh, Heather Whalley, Beatrice Alex (2020). Comparison of Rule-based and Neural Network Models for Negation Detection in Radiology Reports, Journal of Natural Language Engineering, 27(2), pp.203-224. [DOI, accepted manuscript]
- Beatrice Alex, Claire Grover, Richard Tobin, Cathie Sudlow, Grant Mair and William Whiteley (2019). Text Mining Brain Imaging Reports. Journal of Biomedical Semantics, 10, 23, 2019, doi:10.1186/s13326-019-0211-7. [html, pdf]
- Emily Wheater, Grant Mair, Cathie Sudlow, Beatrice Alex, Claire Grover and William Whiteley (2019). A validated natural language processing algorithm for brain imaging phenotypes from radiology reports in UK electronic health records. BMC Medical Informatics and Decision Making, 19, 184, 2019, doi:10.1186/s12911-019-0908-7. [html, pdf]
- Beatrice Alex, Claire Grover, Richard Tobin, Cathie Sudlow, Grant Mair and William Whiteley (2019). Text Mining Brain Imaging Reports, accepted for a special issue, Journal of Biomedical Semantics, 10, pp.1-11. [pdf, DOI]
- Philip John Gorinski, Honghan Wu, Claire Grover, Richard Tobin, Conn Talbot, Heather Whalley, Cathie Sudlow, William Whiteley and Beatrice Alex (2019). Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches, accepted for presentation at the HealTAC 2019 Conference, 24-25th of April 2019. [arXiv.org]
- 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]