Name
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. |
Year | Venue | DocType |
|---|---|---|
2018 | ICML | Conference |
Volume | ISSN | Citations |
abs/1804.03184 | Proceedings of the 35th International Conference on Machine
Learning, PMLR 80:735-744, 2018 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paidamoyo Chapfuwa | 1 | 2 | 1.04 |
| Chenyang Tao | 2 | 8 | 7.93 |
| Chunyuan Li | 3 | 467 | 33.86 |
| Courtney Page | 4 | 0 | 0.34 |
| Benjamin Goldstein | 5 | 0 | 1.01 |
| L. Carin | 6 | 4603 | 339.36 |
| Ricardo Henao | 7 | 286 | 23.85 |