Automated reconciliation of radiology reports and discharge summaries
Koopman, Bevan, Zuccon, Guido, Wagholikar, Amol, Chu, Kevin, O'Dwyer, John, Nguyen, Anthony, & Keijzers, Gerben (2015) Automated reconciliation of radiology reports and discharge summaries. In AMIA Annual Symposium Proceedings, American Medical Informatics Association, San Francisco, CA, pp. 775-784.
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.
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|Item Type:||Conference Paper|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Natural Language Processing (080107)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Health Informatics (080702)
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Deposited On:||20 May 2016 02:11|
|Last Modified:||22 May 2016 22:53|
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