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A Case-Based Approach for the Classification of Medical Time Series

  • Alexander Schlaefer
  • Kay Schröter
  • Lutz Fritsche
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2199)

Abstract

An early and reliable detection of rejections is most important for the successful treatment of renal transplantation patients. A good indicator for the renal function of transplanted patients is the course over time of the parameter creatinine. Existing systems for the analysis of time series usually require frequent and equidistant measurements or a well defined medical theory. These requirements are not fulfilled in our application domain. In this paper we present a case-based approach to classify a creatinine course as critical or non-critical. The distance measure used to find similar cases is based on linear regression. Our results show that while having a good specificity, our sensitivity is significantly higher than that of physicians.

Keywords

Case Base Acute Rejection Electronic Patient Record Temporal Abstraction Intensive Care Unit Environment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Alexander Schlaefer
    • 1
  • Kay Schröter
    • 1
  • Lutz Fritsche
    • 2
  1. 1.Department of Computer Science, Artificial Intelligence GroupHumboldt University BerlinBerlinGermany
  2. 2.Department of NephrologyUniversity Hospital CharitéBerlinGermany

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