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In this talk, we present VIVID, a new statistical framework to better understand complex diseases such as Parkinson's. We will focus on our latest findings to deliver new statistical approaches to identify various types of interpretable feature representations that are prognostically informative in classifying such complex diseases.
We present new and robust ways to visualise valuable information from the thousands of resamples in modern selection methods that use repeated subsampling to identify what features predict best disease progression. We show that using subtractive lack-of-fit measures scales up well to large dimensional situations, making aspects of exhaustive procedures available without its computational cost.
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