# Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework

arXiv: https://arxiv.org/abs/1801.05512 · HTML: https://mlsanity.com/paper/1801.05512/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2018.

## Built on by (most cited first, 30 of 45)

- [Deep Learning for Survival Analysis: A Review](https://mlsanity.com/paper/2305.14961/) (2023)
- [An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes](https://mlsanity.com/paper/2410.01086/) (2024)
- [An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data](https://mlsanity.com/paper/2202.12537/) (2022)
- [SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast](https://mlsanity.com/paper/2403.10603/) (2024)
- [Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration](https://mlsanity.com/paper/2405.07374/) (2024)
- [Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee](https://mlsanity.com/paper/2206.10477/) (2022)
- [Survival Mixture Density Networks](https://mlsanity.com/paper/2208.10759/) (2022)
- [DySurv: dynamic deep learning model for survival analysis with conditional variational inference](https://mlsanity.com/paper/2310.18681/) (2023)
- [Tabular Foundation Models Can Do Survival Analysis](https://mlsanity.com/paper/2601.22259/) (2026)
- [Inverse-Weighted Survival Games](https://mlsanity.com/paper/2111.08175/) (2021)
- [Federated Survival Forests](https://mlsanity.com/paper/2302.02807/) (2023)
- [BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values](https://mlsanity.com/paper/2101.03170/) (2021)
- [Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI](https://mlsanity.com/paper/2204.01702/) (2022)
- [Evidential time-to-event prediction with calibrated uncertainty quantification](https://mlsanity.com/paper/2411.07853/) (2024)
- [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)
- [Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records](https://mlsanity.com/paper/2308.13104/) (2023)
- [Toward Conditional Distribution Calibration in Survival Prediction](https://mlsanity.com/paper/2410.20579/) (2024)
- [A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data](https://mlsanity.com/paper/2101.11935/) (2021)
- [Lymph Node Graph Neural Networks for Cancer Metastasis Prediction](https://mlsanity.com/paper/2106.01711/) (2021)
- [Deep conditional transformation models for survival analysis](https://mlsanity.com/paper/2210.11366/) (2022)
- [Advancing Head and Neck Cancer Survival Prediction via Multi-Label Learning and Deep Model Interpretation](https://mlsanity.com/paper/2405.05488/) (2024)
- [SVL: Goal-Conditioned Reinforcement Learning as Survival Learning](https://mlsanity.com/paper/2604.17551/) (2026)
- [SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference](https://mlsanity.com/paper/2605.15488/) (2026)
- [Multimodal Deep Learning for Personalized Renal Cell Carcinoma Prognosis: Integrating CT Imaging and Clinical Data](https://mlsanity.com/paper/2307.03575/) (2023)
- [Understanding Overparametrization in Survival Models through Interpolation](https://mlsanity.com/paper/2512.12463/) (2025)
- [L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy na"ive breast cancer patients](https://mlsanity.com/paper/2108.10365/) (2021)
- [Advancing Gene Selection in Oncology: A Fusion of Deep Learning and Sparsity for Precision Gene Selection](https://mlsanity.com/paper/2403.01927/) (2024)
- [HuLP: Human-in-the-Loop for Prognosis](https://mlsanity.com/paper/2403.13078/) (2024)
- [Let the Experts Speak: Improving Survival Prediction & Calibration via Mixture-of-Experts Heads](https://mlsanity.com/paper/2511.09567/) (2025)
- [DeepCENT: Prediction of Censored Event Time via Deep Learning](https://mlsanity.com/paper/2202.05155/) (2022)

## Abstract (arXiv)

Survival analysis/time-to-event models are extremely useful as they can help companies predict when a customer will buy a product, churn or default on a loan, and therefore help them improve their ROI. In this paper, we introduce a new method to calculate survival functions using the Multi-Task Logistic Regression (MTLR) model as its base and a deep learning architecture as its core. Based on the Concordance index (C-index) and Brier score, this method outperforms the MTLR in all the experiments disclosed in this paper as well as the Cox Proportional Hazard (CoxPH) model when nonlinear dependencies are found.