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. 2019 Nov 12;10(Suppl 1):23.
doi: 10.1186/s13326-019-0211-7.

Text mining brain imaging reports

Beatrice Alex  1   2   3 , Claire Grover  4   5 , Richard Tobin  4 , Cathie Sudlow  6 , Grant Mair  7 , William Whiteley  7
Affiliations

Text mining brain imaging reports

Beatrice Alex et al. J Biomed Semantics. .

Abstract

Background: With the improvements to text mining technology and the availability of large unstructured Electronic Healthcare Records (EHR) datasets, it is now possible to extract structured information from raw text contained within EHR at reasonably high accuracy. We describe a text mining system for classifying radiologists' reports of CT and MRI brain scans, assigning labels indicating occurrence and type of stroke, as well as other observations. Our system, the Edinburgh Information Extraction for Radiology reports (EdIE-R) system, which we describe here, was developed and tested on a collection of radiology reports.The work reported in this paper is based on 1168 radiology reports from the Edinburgh Stroke Study (ESS), a hospital-based register of stroke and transient ischaemic attack patients. We manually created annotations for this data in parallel with developing the rule-based EdIE-R system to identify phenotype information related to stroke in radiology reports. This process was iterative and domain expert feedback was considered at each iteration to adapt and tune the EdIE-R text mining system which identifies entities, negation and relations between entities in each report and determines report-level labels (phenotypes).

Results: The inter-annotator agreement (IAA) for all types of annotations is high at 96.96 for entities, 96.46 for negation, 95.84 for relations and 94.02 for labels. The equivalent system scores on the blind test set are equally high at 95.49 for entities, 94.41 for negation, 98.27 for relations and 96.39 for labels for the first annotator and 96.86, 96.01, 96.53 and 92.61, respectively for the second annotator.

Conclusion: Automated reading of such EHR data at such high levels of accuracies opens up avenues for population health monitoring and audit, and can provide a resource for epidemiological studies. We are in the process of validating EdIE-R in separate larger cohorts in NHS England and Scotland. The manually annotated ESS corpus will be available for research purposes on application.

Keywords: Electronic healthcare records; Neuroimaging reports; Stroke classification; Text mining.

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Conflict of interest statement

The authors declare that they have no competing interests

Figures

Fig. 1
Fig. 1
Example of entity, relation and negation mark-up
Fig. 2
Fig. 2
EdIE-R processing pipeline
Fig. 3
Fig. 3
An annotated report
Fig. 4
Fig. 4
XML format after document zoning
Fig. 5
Fig. 5
Example lexical entries
Fig. 6
Fig. 6
XML representation of entities and relations in Fig. 1
See this image and copyright information in PMC

References

    1. EdIE-R project page. https://www.ltg.ed.ac.uk/software/edie-r. Accessed 10 July 2019.
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    1. Finkel JR, Grenager T, Manning C. Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics: 2005. p. 363–70. 10.3115/1219840.1219885. - DOI
    1. Cunningham H, Maynard D, Bontcheva K, Tablan V. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. Philadelphia: Association for Computational Linguistics; 2002. GATE: A framework and graphical development environment for robust NLP tools and applications.

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