Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification

Abstract

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.

arXiv commentMachine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216

References in corpus (4)

• Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
• Multi-Scale Convolutional Neural Networks for Time Series Classification
• DeepCare: A Deep Dynamic Memory Model for Predictive Medicine
• Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests

Cited by in corpus (1)

• GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series