Variational Learning of Individual Survival Distributions
This work addresses survival analysis for clinical decision-making, representing an incremental improvement over existing methods.
The authors tackled the problem of predicting time-to-event distributions in survival analysis by introducing Variational Survival Inference (VSI), which relaxes restrictive assumptions and handles censored data, showing improved performance in experiments on synthetic and real-world datasets.
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, {\it 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.