SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models
This provides a tool for interpreting survival model predictions, which is important for domains like healthcare, but it is incremental as it builds on existing explanation and estimation techniques.
The authors tackled the problem of explaining predictions from machine learning survival models by proposing SurvBeNIM, a method that extends the Beran estimator with neural network-based importance functions, and they demonstrated its performance through numerical experiments comparing it to existing explanation methods.
A new method called the Survival Beran-based Neural Importance Model (SurvBeNIM) is proposed. It aims to explain predictions of machine learning survival models, which are in the form of survival or cumulative hazard functions. The main idea behind SurvBeNIM is to extend the Beran estimator by incorporating the importance functions into its kernels and by implementing these importance functions as a set of neural networks which are jointly trained in an end-to-end manner. Two strategies of using and training the whole neural network implementing SurvBeNIM are proposed. The first one explains a single instance, and the neural network is trained for each explained instance. According to the second strategy, the neural network only learns once on all instances from the dataset and on all generated instances. Then the neural network is used to explain any instance in a dataset domain. Various numerical experiments compare the method with different existing explanation methods. A code implementing the proposed method is publicly available.