You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

Complex Measurement Classification in Medical Applications Using a Case-Based Approach

Note:

ISSN 1503-416X

Publication Type:

Conference/Workshop Paper

Venue:

ICCBR03 - The Fifth International Conference on Case-Based Reasoning - Workshop proceedings

Publisher:

Springer


Abstract

In many domains it is sufficient to classify measurements obtained by sensors by filtering noisy data and performing a classification of the measure-ments by means of some mathematical function. Classification of measurements is difficult in some medical domains, e.g. determination of stress levels. Measurements and their classification may be too complex for an algorithmic approach. Matching may result in a number of classification candidates, historic data (previously classified measurements) may be sparse, and same measurements may indicate several diagnoses. We propose a Case-Based Reasoning approach in which feature vectors are matched against cases in a case library, and in which indeterministic or weak classifications are validated by an experienced physician, pointing out the features relevant in the classification of psychophysiological dysfunctions. The physician’s classifications are stored in the case library, continuously improving the system’s performance as it is being used. If only a few examples of classified measurements are available, the experienced physician initially points out which feature combinations are used in the classification/diagnosis.

Bibtex

@inproceedings{Nilsson446,
author = {Markus Nilsson and Peter Funk and Mikael Sollenborn},
title = {Complex Measurement Classification in Medical Applications Using a Case-Based Approach},
note = {ISSN 1503-416X},
editor = {Lorraine McGinty},
pages = {63--73},
month = {June},
year = {2003},
booktitle = {ICCBR03 - The Fifth International Conference on Case-Based Reasoning - Workshop proceedings},
publisher = {Springer},
url = {http://www.es.mdu.se/publications/446-}
}