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Knowledge Discovery and Case Based Reasoning in Medical Applications with Time Series

Authors:

Peter Funk, Markus Nilsson, Ning Xiong

Research groups:

Artificial Intelligence and Intelligent Systems
Learning and Optimisation

Publication Type:

Conference/Workshop Paper

Venue:

In Workshop proceedings of the 6th International Conference on Case Based Reasoning

Abstract

This paper discusses the role and integration of knowledge discovery in case based reasoning systems. The general view is that knowledge discovery is complementary to the task of knowledge retaining and it can be treated as a separate process outside the traditional CBR cycle. Unlike knowledge retaining which is mostly related to case-specific experience, knowledge discovery aims at the elicitation of new knowledge that is more general and valuable by im-proving the functionality of the different CBR sub-steps. The importance of knowledge discovery for CBR is exemplified by a real application scenario in stress medicine in which time series of patterns of breathing cycles are to be analyzed and classified. As single breathing patterns cannot convey reliable in-formation, sequences of patterns are deemed more adequate evidence for diag-nosis by experts. Hence it is advantageous if sequences of patterns can be iden-tified in a CBR approach to a diagnosis and co-occurrence is discovered in categories of patients with similar status.

Bibtex

@inproceedings{Funk812,
author = {Peter Funk and Markus Nilsson and Ning Xiong},
title = {Knowledge Discovery and Case Based Reasoning in Medical Applications with Time Series},
editor = {Isabelle Bichindaritz, Cindy Marling},
month = {December},
year = {2005},
booktitle = {In Workshop proceedings of the 6th International Conference on Case Based Reasoning},
url = {http://www.es.mdu.se/publications/812-}
}