Case-based retrieval to support the treatment of end stage renal failure patients

Stefania Montani, Luigi Portinale, Giorgio Leonardi, Riccardo Bellazzi, Roberto Bellazzi

Research output: Contribution to journalArticlepeer-review

Abstract

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. Materials and 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.

Original languageEnglish
Pages (from-to)31-42
Number of pages12
JournalArtificial Intelligence in Medicine
Volume37
Issue number1
DOIs
Publication statusPublished - May 2006

Keywords

  • Case-based retrieval
  • Hemodialysis
  • Time-series similarity

ASJC Scopus subject areas

  • Artificial Intelligence
  • Medicine(all)

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