# Predicting Survival Outcomes in the Presence of Unlabeled Data

arXiv: https://arxiv.org/abs/2210.13891 · HTML: https://mlsanity.com/paper/2210.13891/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Machine Learning for Survival Analysis: A Survey](https://mlsanity.com/paper/1708.04649/) (2017)
- [On the use of Harrell's C for clinical risk prediction via random survival forests](https://mlsanity.com/paper/1507.03092/) (2015)
- [DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction](https://mlsanity.com/paper/1802.02511/) (2018)
- [A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survival](https://mlsanity.com/paper/1611.05751/) (2016)

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

- [Recent Trends in Artificial Intelligence Technology: A Scoping Review](https://mlsanity.com/paper/2305.04532/) (2023)
- [Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission](https://mlsanity.com/paper/2403.06999/) (2024)
- [Deep Semi-Supervised Survival Analysis for Predicting Cancer Prognosis](https://mlsanity.com/paper/2601.20729/) (2026)

## Abstract (arXiv)

Many clinical studies require the follow-up of patients over time. This is challenging: apart from frequently observed drop-out, there are often also organizational and financial challenges, which can lead to reduced data collection and, in turn, can complicate subsequent analyses. In contrast, there is often plenty of baseline data available of patients with similar characteristics and background information, e.g., from patients that fall outside the study time window. In this article, we investigate whether we can benefit from the inclusion of such unlabeled data instances to predict accurate survival times. In other words, we introduce a third level of supervision in the context of survival analysis, apart from fully observed and censored instances, we also include unlabeled instances. We propose three approaches to deal with this novel setting and provide an empirical comparison over fifteen real-life clinical and gene expression survival datasets. Our results demonstrate that all approaches are able to increase the predictive performance over independent test data. We also show that integrating the partial supervision provided by censored data in a semi-supervised wrapper approach generally provides the best results, often achieving high improvements, compared to not using unlabeled data.