SurvNAM: The machine learning survival model explanation

TL;DR

SurvNAM introduces a novel neural additive model-based approach to explain black-box survival models, enabling both local and global interpretability by approximating complex predictions with generalized additive models.

Contribution

The paper proposes SurvNAM, a new method that extends Neural Additive Models for explaining survival analysis models, incorporating Lasso regularization and specialized network structures.

Findings

SurvNAM effectively explains survival model predictions.

The method achieves high accuracy in local and global explanations.

Numerical experiments demonstrate SurvNAM's efficiency.

Abstract

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.