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We present a generative approach to classify scarcely observed longitudinalpatient trajectories. The available time series are represented as tensors andfactorized using generative deep recurrent neural networks. The learned factorsrepresent the patient data in a compact way and can then be used in adownstream classification task. For more robustness and accuracy in thepredictions, we used an ensemble of those deep generative models to mimicBayesian posterior sampling. We illustrate the performance of our architectureon an intensive-care case study of in-hospital mortality prediction with 96longitudinal measurement types measured across the first 48-hour fromadmission. Our combination of generative and ensemble strategies achieves anAUC of over 0.85, and outperforms the SAPS-II mortality score and GRUbaselines.

Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification