Name
Abstract | ||
|---|---|---|
Previous research has shown that neural networks can model survival data in situations in which some patientsu0027 death times are unknown, e.g. right-censored. However, neural networks have rarely been shown to outperform their linear counterparts such as the Cox proportional hazards model. In this paper, we run simulated experiments and use real survival data to build upon the risk-regression architecture proposed by Faraggi and Simon. We demonstrate that our model, DeepSurv, not only works as well as other survival models but actually outperforms in predictive ability on survival data with linear and nonlinear risk functions. We then show that the neural network can also serve as a recommender system by including a categorical variable representing a treatment group. This can be used to provide personalized treatment recommendations based on an individualu0027s calculated risk. We provide an open source Python module that implements these methods in order to advance research on deep learning and survival analysis. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Machine Learning | Recommender system,Data mining,Nonlinear system,Proportional hazards model,Categorical variable,Artificial intelligence,Deep learning,Artificial neural network,Survival analysis,Machine learning,Python (programming language),Mathematics |
DocType | Volume | Citations |
Journal | abs/1606.00931 | 6 |
PageRank | References | Authors |
0.73 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jared Katzman | 1 | 6 | 0.73 |
| Uri Shaham | 2 | 9 | 1.81 |
| Alexander Cloninger | 3 | 43 | 6.56 |
| Jonathan Bates | 4 | 17 | 2.49 |
| Tingting Jiang | 5 | 280 | 26.85 |
| Yuval Kluger | 6 | 117 | 14.08 |