We present an end-to-end model using streaming physiological time series to accurately predict near-term risk for hypoxemia, a rare, but life-threatening condition known to cause serious patient harm during surgery. Our proposed model makes inference on both hypoxemia outcomes and future input sequences, enabled by a joint sequence autoencoder that simultaneously optimizes a discriminative decoder for label prediction, and two auxiliary decoders trained for data reconstruction and forecast, which seamlessly learns future-indicative latent representation. All decoders share a memory-based encoder that helps capture the global dynamics of patient data. In a large surgical cohort of 73,536 surgeries at a major academic medical center, our model outperforms all baselines and gives a large performance gain over the state-of-the-art hypoxemia prediction system. With a high sensitivity cutoff at 80%, it presents 99.36% precision in predicting hypoxemia and 86.81% precision in predicting the much more severe and rare hypoxemic condition, persistent hypoxemia. With exceptionally low rate of false alarms, our proposed model is promising in improving clinical decision making and easing burden on the health system.
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