Temporal Label Smoothing for Early Event Prediction
METADATA ONLY
Loading...
Author / Producer
Date
2023
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
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.
Permanent link
Publication status
published
Book title
Proceedings of the 40th International Conference on Machine Learning
Journal / series
Volume
202
Pages / Article No.
39913 - 39938
Publisher
PMLR
Event
40th International Conference on Machine Learning (ICML 2023)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09568 - Rätsch, Gunnar / Rätsch, Gunnar
09568 - Rätsch, Gunnar / Rätsch, Gunnar
09664 - Schölkopf, Bernhard / Schölkopf, Bernhard
02219 - ETH AI Center / ETH AI Center
Notes
Funding
Related publications and datasets
Is new version of: