  Given a cardiac-arrest patient being monitored in the ICU (intensive care
unit) for brain activity, how can we predict their health outcomes as early as
possible? Early decision-making is critical in many applications, e.g.
monitoring patients may assist in early intervention and improved care. On the
other hand, early prediction on EEG data poses several challenges: (i)
earliness-accuracy trade-off; observing more data often increases accuracy but
sacrifices earliness, (ii) large-scale (for training) and streaming (online
decision-making) data processing, and (iii) multi-variate (due to multiple
electrodes) and multi-length (due to varying length of stay of patients) time
series. Motivated by this real-world application, we present BeneFitter that
infuses the incurred savings from an early prediction as well as the cost from
misclassification into a unified domain-specific target called benefit.
Unifying these two quantities allows us to directly estimate a single target
(i.e. benefit), and importantly, dictates exactly when to output a prediction:
when benefit estimate becomes positive. BeneFitter (a) is efficient and fast,
with training time linear in the number of input sequences, and can operate in
real-time for decision-making, (b) can handle multi-variate and variable-length
time-series, suitable for patient data, and (c) is effective, providing up to
2x time-savings with equal or better accuracy as compared to competitors.
