# Automated and Interpretable Survival Analysis from Multimodal Data

arXiv: https://arxiv.org/abs/2509.21600 · HTML: https://mlsanity.com/paper/2509.21600/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Deep Learning for Survival Analysis: A Review](https://mlsanity.com/paper/2305.14961/) (2023)
- [SurvLIME: A method for explaining machine learning survival models](https://mlsanity.com/paper/2003.08371/) (2020)
- [Interpretable Machine Learning for Survival Analysis](https://mlsanity.com/paper/2403.10250/) (2024)
- [Disentangled and Interpretable Multimodal Attention Fusion for Cancer Survival Prediction](https://mlsanity.com/paper/2503.16069/) (2025)
- [Improving the efficiency of GP-GOMEA for higher-arity operators](https://mlsanity.com/paper/2402.09854/) (2024)
- [SurvReLU: Inherently Interpretable Survival Analysis via Deep ReLU Networks](https://mlsanity.com/paper/2407.14463/) (2024)
- [A Step towards Interpretable Multimodal AI Models with MultiFIX](https://mlsanity.com/paper/2505.11262/) (2025)

## Built on by (most cited first, 1 of 1)

- [Parallel Adaptive Multi-Objective Evolutionary Learning of Discretized Bayesian Network Classifiers for Clinical Data](https://mlsanity.com/paper/2605.29058/) (2026)

## Abstract (arXiv)

Accurate and interpretable survival analysis remains a core challenge in oncology. With growing multimodal data and the clinical need for transparent models to support validation and trust, this challenge increases in complexity. We propose an interpretable multimodal AI framework to automate survival analysis by integrating clinical variables and computed tomography imaging. Our MultiFIX-based framework uses deep learning to infer survival-relevant features that are further explained: imaging features are interpreted via Grad-CAM, while clinical variables are modeled as symbolic expressions through genetic programming. Risk estimation employs a transparent Cox regression, enabling stratification into groups with distinct survival outcomes. Using the open-source RADCURE dataset for head and neck cancer, MultiFIX achieves a C-index of 0.838 (prediction) and 0.826 (stratification), outperforming the clinical and academic baseline approaches and aligning with known prognostic markers. These results highlight the promise of interpretable multimodal AI for precision oncology with MultiFIX.