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Poster
Temporal Label Smoothing for Early Event Prediction
Hugo Yèche · Alizée Pace · Gunnar Ratsch · Rita Kuznetsova

Wed Jul 26 02:00 PM -- 03:30 PM (PDT) @ Exhibit Hall 1 #121
in Poster Session 3 »

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.

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Author Information

Hugo Yèche (ETH Zürich)
Alizée Pace (ETH Zurich)
Gunnar Ratsch (ETH Zurich)
Rita Kuznetsova (Swiss Federal Institute of Technology)

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