  A new modification of the Neural Additive Model (NAM) called SurvNAM and its
modifications are proposed to explain predictions of the black-box machine
learning survival model. The method is based on applying the original NAM to
solving the explanation problem in the framework of survival analysis. The
basic idea behind SurvNAM is to train the network by means of a specific
expected loss function which takes into account peculiarities of the survival
model predictions and is based on approximating the black-box model by the
extension of the Cox proportional hazards model which uses the well-known
Generalized Additive Model (GAM) in place of the simple linear relationship of
covariates. The proposed method SurvNAM allows performing the local and global
explanation. A set of examples around the explained example is randomly
generated for the local explanation. The global explanation uses the whole
training dataset. The proposed modifications of SurvNAM are based on using the
Lasso-based regularization for functions from GAM and for a special
representation of the GAM functions using their weighted linear and non-linear
parts, which is implemented as a shortcut connection. A lot of numerical
experiments illustrate the SurvNAM efficiency.
