Interval coded scoring systems for survival analysis

摘要

Black-box mathematical models are powerful tools in classification and regression problems. Thanks to the use of (unknown) transformations of the inputs, the outcome can be estimated, improving performance in comparison to standard statistical models. A disadvantage of these complex models however, is their lack of interpretability. This work illustrates how advanced methods can be made interpretable. Using constant B-spline kernel functions and sparsity constraints, interval coded scoring models for survival analysis are presented.