DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive Models
This addresses the need for interpretable and calibrated survival models in healthcare, though it is incremental as it builds on existing glass-box methods.
The authors tackled the problem of interpretable survival analysis in healthcare by introducing DNAMite, a glass-box model that produces calibrated shape functions with flexible smoothness, achieving comparable predictive performance and better calibration than prior models.
Survival analysis is a classic problem in statistics with important applications in healthcare. Most machine learning models for survival analysis are black-box models, limiting their use in healthcare settings where interpretability is paramount. More recently, glass-box machine learning models have been introduced for survival analysis, with both strong predictive performance and interpretability. Still, several gaps remain, as no prior glass-box survival model can produce calibrated shape functions with enough flexibility to capture the complex patterns often found in real data. To fill this gap, we introduce a new glass-box machine learning model for survival analysis called DNAMite. DNAMite uses feature discretization and kernel smoothing in its embedding module, making it possible to learn shape functions with a flexible balance of smoothness and jaggedness. Further, DNAMite produces calibrated shape functions that can be directly interpreted as contributions to the cumulative incidence function. Our experiments show that DNAMite generates shape functions closer to true shape functions on synthetic data, while making predictions with comparable predictive performance and better calibration than previous glass-box and black-box models.