  We present a generative approach to classify scarcely observed longitudinal
patient trajectories. The available time series are represented as tensors and
factorized using generative deep recurrent neural networks. The learned factors
represent the patient data in a compact way and can then be used in a
downstream classification task. For more robustness and accuracy in the
predictions, we used an ensemble of those deep generative models to mimic
Bayesian posterior sampling. We illustrate the performance of our architecture
on an intensive-care case study of in-hospital mortality prediction with 96
longitudinal measurement types measured across the first 48-hour from
admission. Our combination of generative and ensemble strategies achieves an
AUC of over 0.85, and outperforms the SAPS-II mortality score and GRU
baselines.
