Interpretable Survival

Interpretable survival analysis aims to build accurate predictive models for time-to-event data while simultaneously providing insights into the factors driving those predictions. Current research focuses on developing and improving both inherently interpretable models, such as those based on tree structures or modified Cox proportional hazards models, and methods for explaining the predictions of more complex "black box" models like deep neural networks using techniques like SHAP values or gradient-based visualizations. This field is crucial for advancing healthcare applications, enabling clinicians to understand and trust AI-driven predictions for personalized treatment and improved patient outcomes. The development of robust and transparent survival models is essential for responsible and effective use of AI in medicine.