Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework
This work addresses survival prediction for businesses to improve ROI, but it is incremental as it builds on existing MTLR with deep learning enhancements.
The paper tackles survival analysis by proposing a deep learning method based on Multi-Task Logistic Regression, which outperforms baseline models like MTLR and CoxPH in experiments, achieving higher C-index and lower Brier scores.
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.