| Abstract: | In this paper, we present a case-based retrieval system called RHENE (Retrieval of HEmodialysis in NEphrological disorders) working in the domain of patients affected by nephropatologies and treated with hemodialysis. 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.
In RHENE, retrieval relies upon a multi-step procedure. In particular, a preliminary classification step, based on static features, reduces the retrieval search space. Intra-class retrieval then takes place by considering dynamic features, and is articulated as follows: (1) "locally" similar cases (considering one feature at a time) are extracted and the intersection of the retrieved sets is computed; (2) "global" similarity is computed - as a weighted average of local distances - and the best cases are listed.
The main goal of the paper is to present an approach for efficiently implementing step (2), by taking into account specific information regarding the final application. 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; as mentioned above results of such local queries are then suitably combined, allowing the physician to exhamine the most similar stored dialysis sessions with respect to the current one. The system can be seen as a support for patient examination and therapy evaluation, but could also be adopted as a means for assessing the quality of the overall hemodialysis service, providing a useful input from the knowledge management perspective. |
