# Random Survival Forest for Censored Functional Data

arXiv: https://arxiv.org/abs/2407.15340 · HTML: https://mlsanity.com/paper/2407.15340/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [Machine Learning for Survival Analysis: A Survey](https://mlsanity.com/paper/1708.04649/) (2017)
- [Combining parametric, semi-parametric, and non-parametric survival models with stacked survival models](https://mlsanity.com/paper/1309.7936/) (2013)
- [Consistency of survival tree and forest models: splitting bias and correction](https://mlsanity.com/paper/1707.09631/) (2017)

## Built on by (most cited first, 3 of 3)

- [Augmented Functional Random Forests: Classifier Construction and Unbiased Functional Principal Components Importance through Ad-Hoc Conditional Permutations](https://mlsanity.com/paper/2408.13179/) (2024)
- [Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests](https://mlsanity.com/paper/2504.18498/) (2025)
- [Functional Clustering of Survival Data via Smoothed Log-Hazard Trajectories: A Risk-Dynamics Perspective](https://mlsanity.com/paper/2606.01239/) (2026)

## Abstract (arXiv)

This paper introduces a Random Survival Forest (RSF) method for functional data. The focus is specifically on defining a new functional data structure, the Censored Functional Data (CFD), for dealing with temporal observations that are censored due to study limitations or incomplete data collection. This approach allows for precise modelling of functional survival trajectories, leading to improved interpretation and prediction of survival dynamics across different groups. A medical survival study on the benchmark SOFA data set is presented. Results show good performance of the proposed approach, particularly in ranking the importance of predicting variables, as captured through dynamic changes in SOFA scores and patient mortality rates.