  We present an end-to-end model using streaming physiological time series to
predict near-term risk for hypoxemia, a rare, but life-threatening condition
known to cause serious patient harm during surgery. Inspired by the fact that a
hypoxemia event is defined based on a future sequence of low SpO2 (i.e., blood
oxygen saturation) instances, we propose the hybrid inference network (hiNet)
that makes hybrid inference on both future low SpO2 instances and hypoxemia
outcomes. hiNet integrates 1) a joint sequence autoencoder that simultaneously
optimizes a discriminative decoder for label prediction, and 2) two auxiliary
decoders trained for data reconstruction and forecast, which seamlessly learn
contextual latent representations that capture the transition from present
states to future states. All decoders share a memory-based encoder that helps
capture the global dynamics of patient measurement. For a large surgical cohort
of 72,081 surgeries at a major academic medical center, our model outperforms
strong baselines including the model used by the state-of-the-art hypoxemia
prediction system. With its capability to make real-time predictions of
near-term hypoxemic at clinically acceptable alarm rates, hiNet shows promise
in improving clinical decision making and easing burden of perioperative care.
