Adversarial Time-to-Event Modeling

Abstract

Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice. Time-to-event analysis, also called survival analysis, stands as one of the most representative examples of such statistical models. We present a deep-network-based approach that leverages adversarial learning to address a key challenge in modern time-to-event modeling: nonparametric estimation of event-time distributions. We also introduce a principled cost function to exploit information from censored events (events that occur subsequent to the observation window). Unlike most time-to-event models, we focus on the estimation of time-to-event distributions, rather than time ordering. We validate our model on both benchmark and real datasets, demonstrating that the proposed formulation yields significant performance gains relative to a parametric alternative, which we also propose.

arXiv commentPublished in ICML 2018; Code: https://github.com/paidamoyo/adversarial_time_to_event

References in corpus (8)

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Brain Tumor Segmentation with Deep Neural Networks
• Generative Adversarial Text to Image Synthesis
• Improved Techniques for Training GANs
• Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
• Adversarial Feature Matching for Text Generation
• Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework
• Predicting accurate probabilities with a ranking loss

Cited by in corpus (9)

• Enabling Counterfactual Survival Analysis with Balanced Representations
• Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities
• SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks
• Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks
• A General Framework for Survival Analysis and Multi-State Modelling
• Federated Survival Analysis with Discrete-Time Cox Models
• Deep Lifetime Clustering
• SAFE: A Neural Survival Analysis Model for Fraud Early Detection
• Recurrent Adversarial Service Times