Abstract
A growing number of patients suffer from end stage renal failure. For these patients either transplantation or regular dialysis treatment is necessary for survival. But the cost of providing dialysis care is high and the survival span of patients on dialysis is significantly lower than for patients with transplants. During dialysis two dozen parameters can be observed on a regular basis. Analyzing these data with data mining algorithms can help to identify critical factors for patient survival.
The paper provides a brief review of state-of-the-art methods for predicting patient risk as well as some new ideas. Its main contribution lies in the incorporation of the underlying temporal structure of the dialysis data where other studies consider only aggregated values. All methods are evaluated on real-world data from dialysis clinics in Southern and Eastern Europe.
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© 2009 Springer-Verlag Berlin Heidelberg
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Knorr, T., Schmidt-Thieme, L., Johner, C. (2009). Identifying Patients at Risk. In: Gaul, W., Bock, HH., Imaizumi, T., Okada, A. (eds) Cooperation in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00668-5_14
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DOI: https://doi.org/10.1007/978-3-642-00668-5_14
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Online ISBN: 978-3-642-00668-5
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