By Topic

Challenges and techniques for mining real clinical data

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
$33 $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

1 Author(s)
Wesley W. Chu ; University of California, Los Angeles, USA

Regression analysis and statistical hypothesis testing are commonly used for association and classification of clinical data sets in medical studies. Although such traditional techniques are wildly used, they have several shortcomings. For example, when analyzing datasets with a large number of temporal attributes, domain experts often miss important associative attributes in regression analysis because of the large number of correlated attributes. On the other hand, for rare occurring diseases or operations, the number of documented observed cases is usually small, and hypothesis testing becomes ineffective for such analysis due to insufficient statistical significance. We shall present two such case studies to showcase how data mining techniques [1-7] can be used to remedy such shortcomings.

Published in:

Granular Computing, 2008. GrC 2008. IEEE International Conference on

Date of Conference:

26-28 Aug. 2008