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.

View at publisher (open access)

Abstract

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.

Impact and interest:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 95678
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
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

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page