The abundance of modern health data provides many opportunities for the use of machine learning techniques to build better statistical models to improve clinical decision making. Predicting time-to-event distributions, also known as survival analysis, plays a key role in many clinical applications. We introduce a variational time-to-event prediction model, named Variational Survival Inference (VSI), which builds upon recent advances in distribution learning techniques and deep neural networks. VSI addresses the challenges of non-parametric distribution estimation by ($i$) relaxing the restrictive modeling assumptions made in classical models, and ($ii$) efficiently handling the censored observations, i.e. events that occur outside the observation window, all within the variational framework. To validate the effectiveness of our approach, an extensive set of experiments on both synthetic and real-world datasets is carried out, showing improved performance relative to competing solutions.
|Original language||English (US)|
|Title of host publication||ACM CHIL 2020 - Proceedings of the 2020 ACM Conference on Health, Inference, and Learning|
|Publisher||Association for Computing Machinery, Incacmhelp@acm.org|
|Number of pages||9|
|State||Published - Feb 4 2020|