Deep Parametric Time-to-Event Regression with Time-Varying Covariates

Time-to-event regression in healthcare and other domains, such as predictive maintenance, require working with time-series (or time-varying) data such as continuously monitored vital signs, electronic health records, or sensor readings. In such scenarios, the event-time distribution may have temporal dependencies at different time scales that are not easily captured by classical survival models that assume training data points to be independent. In this paper, we describe a fully parametric approach to model censored time-to-event outcomes with time varying covariates. It involves learning representations of the input temporal data using Recurrent Neural Networks such as LSTMs and GRUs, followed by describing the conditional event distribution as a fixed mixture of parametric distributions. The use of the recurrent neural networks allows the learned representations to model long-term dependencies in the input data while jointly estimating the Time-to-Event. We benchmark our approach on MIMIC III: a large, publicly available dataset collected from Intensive Care Unit (ICU) patients, focusing on predicting duration of their ICU stays and their short term life expectancy, and we demonstrate competitive performance of the proposed approach compared to established time-to-event regression models.(1)