Please use this identifier to cite or link to this item:
Title: Text mining in radiology reports
Authors: Gong, T.
Tan, C.L. 
Yun, L.T. 
Lee, C.K.
Pang, B.C.
Lim, C.C.T.
Tian, Q.
Tang, S.
Zhang, Z.
Issue Date: 2008
Citation: Gong, T., Tan, C.L., Yun, L.T., Lee, C.K., Pang, B.C., Lim, C.C.T., Tian, Q., Tang, S., Zhang, Z. (2008). Text mining in radiology reports. Proceedings - IEEE International Conference on Data Mining, ICDM : 815-820. ScholarBank@NUS Repository.
Abstract: Medical text mining has gained increasing interest in recent years. Radiology reports contain rich information describing radiologist's observations on the patient's medical conditions in the associated medical images. However, as most reports are in free text format, the valuable information contained in those reports cannot be easily accessed and used, unless proper text mining has been applied. In this paper, we propose a text mining system to extract and use the information in radiology reports. The system consists of three main modules: a medical finding extractor, a report and image retriever, and a text-assisted image feature extractor. In evaluation, the overall precision and recall for medical finding extraction are 95.5% and 87.9% respectively, and for all modifiers of the medical findings 88.2% and 82.8% respectively. The overall result of report and image retrieval module and text-assisted image feature extraction module is satisfactory to radiologists. ©2008 IEEE.
Source Title: Proceedings - IEEE International Conference on Data Mining, ICDM
ISBN: 9780769535029
ISSN: 15504786
DOI: 10.1109/ICDM.2008.150
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Mar 18, 2019


checked on Mar 18, 2019

Page view(s)

checked on Feb 9, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.