A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data
This work addresses a gap in variational autoencoders for handling heterogeneous data in domains like healthcare, but it is incremental as it extends existing temporal VAEs.
The authors tackled the problem of modeling heterogeneous temporal and longitudinal data, which includes mixed continuous and discrete attributes, by proposing the HL-VAE model, achieving competitive performance in missing value imputation and predictive accuracy on simulated and clinical datasets.
The variational autoencoder (VAE) is a popular deep latent variable model used to analyse high-dimensional datasets by learning a low-dimensional latent representation of the data. It simultaneously learns a generative model and an inference network to perform approximate posterior inference. Recently proposed extensions to VAEs that can handle temporal and longitudinal data have applications in healthcare, behavioural modelling, and predictive maintenance. However, these extensions do not account for heterogeneous data (i.e., data comprising of continuous and discrete attributes), which is common in many real-life applications. In this work, we propose the heterogeneous longitudinal VAE (HL-VAE) that extends the existing temporal and longitudinal VAEs to heterogeneous data. HL-VAE provides efficient inference for high-dimensional datasets and includes likelihood models for continuous, count, categorical, and ordinal data while accounting for missing observations. We demonstrate our model's efficacy through simulated as well as clinical datasets, and show that our proposed model achieves competitive performance in missing value imputation and predictive accuracy.