Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework
Published in
arXiv.org(2018)External Links:
TL
TL;DR
A new method to calculate survival functions using the Multi-Task Logistic Regression model as its base and a deep learning architecture as its core, which outperforms the MTLR in all the experiments disclosed in this paper.
Abstract
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.
References24 items
1
Deep Learning for Patient-Specific Kidney Graft Survival Analysis
2
A Probabilistic Multi-Touch Attribution Model for Online Advertising
3
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles
4
Time to default in credit scoring using survival analysis: a benchmark study
5