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A prediction method for multi-class systems based on limited data [clinical trials]

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2 Author(s)
Kuznetsov, V.A. ; Lab. Integrative & Med. Biophys., Nat. Inst. of Health, Bethesda, MD, USA ; Knott, G.D.

In many clinical trials, the prediction of patient outcome following therapy requires the analysis of two or more small groups of responders having a large number of simultaneously measured covariates, some of whose values may be absent. Prediction of individual outcomes in these groups is a severe statistical problem. This has motivated us to develop a suitable approach for inference from such limited data. A new statistically-oriented prediction method, called optimized independent segment voting (OISV), is presented for constructing a class-membership prediction function for such data sets. This “voting” prediction function is constructed based on the most informative and robust discrete segments of all covariate ranges, which are thus discretized

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Computer-Based Medical Systems, 2001. CBMS 2001. Proceedings. 14th IEEE Symposium on

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