By Topic

Multi-topic Aspects in Clinical Text Classification

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

3 Author(s)

This paper investigates multi-topic aspects in automatic classification of clinical free text. In many practical situ- ations, we need to deal with documents overlapping with multiple topics. Automatic assignment of multiple ICD-9- CM codes to clinical free text in medical records is a typi- cal multi-topic text classification problem. In this paper, we facilitate two different views on multi-topics. The Closed Topic Assumption (CTA) regards an absence of topics for a document as an explicit declaration that this document does not belong to those absent topics. In contrast, the Open Topic Assumption (OTA) considers the missing topics as neutral topics. This paper compares performances of vari- ous interpretations of a multi-topic Text Classification prob- lem into a Machine Learning problem. Experimental results show that the characteristics of multi-topic assignments in the Medical NLP Challenge data is OTA-oriented.

Published in:

Bioinformatics and Biomedicine, 2007. BIBM 2007. IEEE International Conference on

Date of Conference:

2-4 Nov. 2007