Case-based retrieval to support the treatment of end stage renal failure patients.
OBJECTIVE
In the present paper, we describe an application of case-based retrieval to the domain of end stage renal failure patients, treated with hemodialysis.


METHODS
Defining a dialysis session as a case, retrieval of past similar cases has to operate both on static and on dynamic features, since most of the monitoring variables of a dialysis session are time series. Retrieval is then articulated as a two-step procedure: (1) classification, based on static features and (2) intra-class retrieval, in which dynamic features are considered. As regards step (2), we concentrate on a classical dimensionality reduction technique for time series allowing for efficient indexing, namely discrete Fourier transform (DFT). Thanks to specific index structures (i.e. k -d trees), range queries (on local feature similarity) can be efficiently performed on our case base, allowing the physician to examine the most similar stored dialysis sessions with respect to the current one.


RESULTS
The retrieval tool has been positively tested on real patients' data, coming from the nephrology and dialysis unit of the Vigevano hospital, in Italy.


CONCLUSIONS
The overall system can be seen as a means for supporting quality assessment of the hemodialysis service, providing a useful input from the knowledge management perspective.
Montani
Stefania
S
Dipartimento di Informatica, Università del Piemonte Orientale, Via Bellini 25/g, 15100 Alessandria, Italy.
Portinale
Luigi
L
Leonardi
Giorgio
G
Bellazzi
Riccardo
R
Bellazzi
Roberto
R
D020000:Decision Support Systems, Clinical; D006436:Hemodialysis Units, Hospital; D006751:Hospital Information Systems; D006801:Humans; D016247:Information Storage and Retrieval; D007676:Kidney Failure, Chronic / Q000145:classification / Q000628:therapy*; D015233:Models, Statistical; D006435:Renal Dialysis; D013813:Therapy, Computer-Assisted