Learning Covariate Relations in Disease Progression Models Using Symbolic Neural Networks

Covariate modeling provides individual predictions of outcomes by disease progression models. Current methodology for mapping covariates onto model parameters is limited by predefined parametric functions which can result in inadequate covariate selection and biased predictions by the final model. Furthermore, present methodology scales poorly to high-dimensional data due to combinatorial limitations. In the present study, a novel method for automation of covariate model identification in diseas