By Topic

Clinical Decision Support for Colonoscopy Surveillance Using Natural Language Processing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

11 Author(s)
Wagholikar, K. ; Biomed. Stat. & Inf., Mayo Clinic Rochester, Rochester, MN, USA ; Sunghwan Sohn ; Wu, S. ; Kaggal, V.
more authors

Colorectal cancer is the second leading cause of cancer-related deaths in the United States. However, 41% of patients do not receive adequate screening, since the surveillance guidelines for colonoscopy are complex and are not easily recalled by health care providers. As a potential solution, we developed a guideline based clinical decision support system (CDSS) that can interpret relevant freetext reports including indications, pathology and procedure notes. The CDSS was evaluated by comparing its recommendations with those of a gastroenterologist for a test set of 53 patients. The CDSS made the optimal recommendation in 48 cases, and helped the gastroenterologist revise the recommendation in 3 cases. We performed an error analysis for the 5 failure cases, and subsequently were able to modify the CDSS to output the correct recommendation for all the test cases. Results indicate that the system has a high potential for clinical deployment, but further evaluation and optimization is required. Limitations of our study are that the study was conducted at a single institution and with a single expert, and the evaluation did not include rare decision scenarios. Overall our work demonstrates the utility of natural language processing to enhance clinical decision support.

Published in:

Healthcare Informatics, Imaging and Systems Biology (HISB), 2012 IEEE Second International Conference on

Date of Conference:

27-28 Sept. 2012