Automated Reconciliation of Radiology Reports and Discharge Summaries

Bevan Koopman, Guido Zuccon, Amol Wagholikar, Kevin Chu, John O'Dwyer, Anthony Nguyen, Gerben Keijzers

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

11 Citations (Scopus)


We study machine learning techniques to automatically identify limb abnormalities (including fractures, dislocations and foreign bodies) from radiology reports. For patients presenting to the Emergency Room (ER) with suspected limb abnormalities (e.g., fractures) there is often a multi-day delay before the radiology report is available to ER staff, by which time the patient may have been discharged home with the possibility of undiagnosed fractures. ER staff, currently, have to manually review and reconcile radiology reports with the ER discharge diagnosis; this is a laborious and error-prone manual process. Using radiology reports from three different hospitals, we show that extracting detailed features from the reports to train Support Vector Machines can effectively automate the identification of limb fractures, dislocations and foreign bodies. These can be automatically reconciled with a patient's discharge diagnosis from the ER to identify a number of cases where limb abnormalities went undiagnosed.

Original languageEnglish
Title of host publicationAMIA Annual Symposium Proceedings
Number of pages10
Publication statusPublished - 1 Jan 2015
Externally publishedYes

Publication series

NameAMIA ... Annual Symposium proceedings. AMIA Symposium
PublisherAmerican Medical Informatics Association
ISSN (Print)1559-4076


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