# A General Machine Learning Framework for Survival Analysis

arXiv: https://arxiv.org/abs/2006.15442 · HTML: https://mlsanity.com/paper/2006.15442/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [Deep Learning for Survival Analysis: A Review](https://mlsanity.com/paper/2305.14961/) (2023)
- [DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis](https://mlsanity.com/paper/2202.07423/) (2022)
- [Federated Survival Forests](https://mlsanity.com/paper/2302.02807/) (2023)
- [Nonparametric Conditional Local Independence Testing](https://mlsanity.com/paper/2203.13559/) (2022)
- [Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics](https://mlsanity.com/paper/2308.02382/) (2023)
- [Survival Analysis as Imprecise Classification with Trainable Kernels](https://mlsanity.com/paper/2506.10140/) (2025)
- [Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation](https://mlsanity.com/paper/2110.11312/) (2021)
- [SurvDiff: A Diffusion Model for Generating Synthetic Data in Survival Analysis](https://mlsanity.com/paper/2509.22352/) (2025)
- [Survival-oriented embeddings for improving accessibility to complex data structures](https://mlsanity.com/paper/2110.11303/) (2021)
- [Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models](https://mlsanity.com/paper/2210.07723/) (2022)
- [Reduction Techniques for Survival Analysis](https://mlsanity.com/paper/2508.05715/) (2025)
- [TabSurv: Adapting Modern Tabular Neural Networks to Survival Analysis](https://mlsanity.com/paper/2605.03944/) (2026)

## Abstract (arXiv)

The modeling of time-to-event data, also known as survival analysis, requires specialized methods that can deal with censoring and truncation, time-varying features and effects, and that extend to settings with multiple competing events. However, many machine learning methods for survival analysis only consider the standard setting with right-censored data and proportional hazards assumption. The methods that do provide extensions usually address at most a subset of these challenges and often require specialized software that can not be integrated into standard machine learning workflows directly. In this work, we present a very general machine learning framework for time-to-event analysis that uses a data augmentation strategy to reduce complex survival tasks to standard Poisson regression tasks. This reformulation is based on well developed statistical theory. With the proposed approach, any algorithm that can optimize a Poisson (log-)likelihood, such as gradient boosted trees, deep neural networks, model-based boosting and many more can be used in the context of time-to-event analysis. The proposed technique does not require any assumptions with respect to the distribution of event times or the functional shapes of feature and interaction effects. Based on the proposed framework we develop new methods that are competitive with specialized state of the art approaches in terms of accuracy, and versatility, but with comparatively small investments of programming effort or requirements for specialized methodological know-how.