Interpretable Machine Learning for Survival Analysis
Sophie Hanna Langbein Mateusz Krzyziński Mikołaj Spytek Hubert Baniecki Przemysław Biecek Marvin N. Wright
With the spread and rapid advancement of black box machine learning models,
the field of interpretable machine learning (IML) or explainable artificial
intelligence (XAI) has become increasingly important over the last decade. This
is particularly relevant for survival analysis, where the adoption of IML
techniques promotes transparency, accountability and fairness in sensitive
areas, such as clinical decision making processes, the development of targeted
therapies, interventions or in other medical or healthcare related contexts.
More specifically, explainability can uncover a survival model's potential
biases and limitations and provide more mathematically sound ways to understand
how and which features are influential for prediction or constitute risk
factors. However, the lack of readily available IML methods may have deterred
medical practitioners and policy makers in public health from leveraging the
full potential of machine learning for predicting time-to-event data. We
present a comprehensive review of the limited existing amount of work on IML
methods for survival analysis within the context of the general IML taxonomy.
In addition, we formally detail how commonly used IML methods, such as such as
individual conditional expectation (ICE), partial dependence plots (PDP),
accumulated local effects (ALE), different feature importance measures or
Friedman's H-interaction statistics can be adapted to survival outcomes. An
application of several IML methods to real data on data on under-5 year
mortality of Ghanaian children from the Demographic and Health Surveys (DHS)
Program serves as a tutorial or guide for researchers, on how to utilize the
techniques in practice to facilitate understanding of model decisions or
predictions.