  We present a scalable end-to-end classifier that uses streaming physiological
and medication data to accurately predict the onset of sepsis, a
life-threatening complication from infections that has high mortality and
morbidity. Our proposed framework models the multivariate trajectories of
continuous-valued physiological time series using multitask Gaussian processes,
seamlessly accounting for the high uncertainty, frequent missingness, and
irregular sampling rates typically associated with real clinical data. The
Gaussian process is directly connected to a black-box classifier that predicts
whether a patient will become septic, chosen in our case to be a recurrent
neural network to account for the extreme variability in the length of patient
encounters. We show how to scale the computations associated with the Gaussian
process in a manner so that the entire system can be discriminatively trained
end-to-end using backpropagation. In a large cohort of heterogeneous inpatient
encounters at our university health system we find that it outperforms several
baselines at predicting sepsis, and yields 19.4% and 55.5% improved areas under
the Receiver Operating Characteristic and Precision Recall curves as compared
to the NEWS score currently used by our hospital.
