  Machine prediction algorithms (e.g., binary classifiers) often are adopted on
the basis of claimed performance using classic metrics such as sensitivity and
predictive value. However, classifier performance depends heavily upon the
context (workflow) in which the classifier operates. Classic metrics do not
reflect the realized utility of a predictor unless certain implicit assumptions
are met, and these assumptions cannot be met in many common clinical scenarios.
This often results in suboptimal implementations and in disappointment when
expected outcomes are not achieved. One common failure mode for classic metrics
arises when multiple predictions can be made for the same event, particularly
when redundant true positive predictions produce little additional value. This
describes many clinical alerting systems. We explain why classic metrics cannot
correctly represent predictor performance in such contexts, and introduce an
improved performance assessment technique using utility functions to score
predictions based on their utility in a specific workflow context. The
resulting utility metrics (u-metrics) explicitly account for the effects of
temporal relationships on prediction utility. Compared to traditional measures,
u-metrics more accurately reflect the real world costs and benefits of a
predictor operating in a live clinical context. The improvement can be
significant. We also describe a formal approach to snoozing, a mitigation
strategy in which some predictions are suppressed to improve predictor
performance by reducing false positives while retaining event capture. Snoozing
is especially useful for predictors that generate interruptive alarms.
U-metrics correctly measure and predict the performance benefits of snoozing,
whereas traditional metrics do not.
