University of Birmingham > Talks@bham > Centre for Computational Biology Seminar Series > Text mining radiology reports

Text mining radiology reports

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If you have a question about this talk, please contact Dr Christopher Yau.

With 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. This talk will present text mining methods for classifying radiologists’ reports of CT and MRI brain scans, assigning labels indicating occurrence and type of stroke, as well as other observations. While such methods have already been applied to the Edinburgh Stroke Study data collected by NHS Lothian as well as radiology reports created in NHS Tayside, we are in the process of scaling this type of processing up to much larger datasets, e.g. Scotland-wide, or apply it to specific cohort, e.g. Generation Scotland. Automated reading of EHR data at high levels of accuracies opens up avenues for population health monitoring and audit, and can provide a resource for epidemiological studies.

This talk is part of the Centre for Computational Biology Seminar Series series.

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