  Models that can predict the occurrence of events ahead of time with low
false-alarm rates are critical to the acceptance of decision support systems in
the medical community. This challenging task is typically treated as a simple
binary classification, ignoring temporal dependencies between samples, whereas
we propose to exploit this structure. We first introduce a common theoretical
framework unifying dynamic survival analysis and early event prediction.
Following an analysis of objectives from both fields, we propose Temporal Label
Smoothing (TLS), a simpler, yet best-performing method that preserves
prediction monotonicity over time. By focusing the objective on areas with a
stronger predictive signal, TLS improves performance over all baselines on two
large-scale benchmark tasks. Gains are particularly notable along clinically
relevant measures, such as event recall at low false-alarm rates. TLS reduces
the number of missed events by up to a factor of two over previously used
approaches in early event prediction.

Machine Learning