Abstract
Medical knowledge in the form of causal sequences of diseases can be used in designing ranked models. Considered ranked models are based on linear transformation of multivariate feature vectors on a line that preserves in a best possible way a causal order between diseases. Clinical data sets from particular diseases supplied with a causal order within pairs of these diseases may be used in the definition of the convex and piecewise linear (CPL) criterion function. The linear ranked transformations can be designed through minimization of the CPL criterion functions.
This work was partially supported by the KBN grant 3T11F01130, by the grant 16/St/2007 from the Institute of Biocybernetics and Biomedical Engineering PAS, and by the grant W/II/1/2007 from the Białystok University of Technology.
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Bobrowski, L., Łukaszuk, T., Wasyluk, H. (2009). Ranked Modeling of Causal Sequences of Diseases for the Purpose of Early Diagnosis. In: Kącki, E., Rudnicki, M., Stempczyńska, J. (eds) Computers in Medical Activity. Advances in Intelligent and Soft Computing, vol 65. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04462-5_3
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DOI: https://doi.org/10.1007/978-3-642-04462-5_3
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