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A key question in medical decision support is how best to visualise a patient database, with especial reference to cohort labelling, whether this is an indicator function for classification or a cluster index. We propose the use of the kernel trick to visualise complete patient databases, in low-dimensional projections, with class labelling, given a non-linear classifier of choice. The results show that this method is useful both to see how individual patient cases relate to each other with reference to the classification boundary, and also to obtain a visual indication of the separation that can be obtained with difference choices of kernel functions.
Date of Conference: 18-23 July 2010