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Concise case indexing of time series in health care by means of key sequence discovery

Fulltext:


Publication Type:

Journal article

Venue:

Applied Intelligence


Abstract

Coping with time series cases is becoming an important issue in applications of case based reasoning in medical cares. This paper develops a knowledge discovery approach to discovering significant sequences for depicting symbolic time series cases. The input is a case library containing time series cases consisting of consecutive discrete patterns. The proposed approach is able to find from the given case library all qualified sequences that are nonredundant and indicative. A sequence as such is termed as a key sequence. It is shown that the key sequences discovered are highly valuable in case characterization to capture important properties while ignoring random trivialities. The main idea is to transform an original (lengthy) time series into a more concise representation in terms of the detected occurrences of key sequences. Four alternative ways to develop case indexes based on key sequences are suggested and discussed in detail. These indexes are simply vectors of numbers that are easily usable when matching two time series cases for case retrieval. Preliminary experiment results have revealed that such case indexes utilizing key sequence information result in substantial performance improvement for the underlying case-based reasoning system.

Bibtex

@article{Xiong2297,
author = {Ning Xiong and Peter Funk},
title = {Concise case indexing of time series in health care by means of key sequence discovery},
volume = {28},
pages = {247--260},
month = {June},
year = {2008},
journal = {Applied Intelligence},
url = {http://www.es.mdh.se/publications/2297-}
}