Papers › Temporal Label Smoothing for Early Event Prediction
Temporal Label Smoothing for Early Event Prediction
Hugo Yèche, Alizée Pace, Gunnar Rätsch, Rita Kuznetsova
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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Code
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Code Syntology ran Syntology
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Circulatory Failure | HiRID | Temporal Label Smoothing | AUPRC | 0.406±0.003 | #1 of 8 | Archive leaderboard | report |
| Circulatory Failure | HiRID | Temporal Label Smoothing | Recall@50 | 32.3 | #1 of 8 | Archive leaderboard | report |
| Respiratory Failure | HiRID | Temporal Label Smoothing | AUPRC | 0.604±0.002 | #1 of 8 | Archive leaderboard | report |
| Respiratory Failure | HiRID | Temporal Label Smoothing | Recall@50 | 77.0 | #1 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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