Transfer learning with deployment-covariate recalibration for survival prediction under covariate shift
Ranking
Overall
82
Content
95
Popularity
50
Observed public metrics from 1 member.
Merged summary
TL;DR - Pan et al. introduce CoxRTL, a transfer-learning method for survival prediction that recalibrates external cohort data under covariate shift. It targets settings with limited training data and no observed deployment outcomes.
- Uses external cohorts to improve target-cohort survival prediction.
- Recalibrates for differences between training and deployment covariates.
- Designed to work without deployment outcome labels.
- Addresses data scarcity in time-to-event modeling.
Sources (1)
Transfer learning with deployment-covariate recalibration for survival prediction under covariate shift
Public signals
OpenAlex citations 0
TL;DR - Pan et al. introduce CoxRTL, a transfer-learning method for survival prediction that recalibrates external cohort data under covariate shift. It targets settings with limited training data and no observed deployment outcomes.
- Uses external cohorts to improve target-cohort survival prediction.
- Recalibrates for differences between training and deployment covariates.
- Designed to work without deployment outcome labels.
- Addresses data scarcity in time-to-event modeling.