Reposted by dr feelgood
Transfer learning with deployment-covariate recalibration for survival prediction under covariate shift
->Nature | More on "Transfer learning for survival prediction" at BigEarthData.ai
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Transfer learning with deployment-covariate recalibration for survival prediction under covariate shift
Extended Data Fig. 1 Wasserstein distances between external, target, and deployment cohorts before and after covariate-shift adjustment across 20 diseases. Wasserstein distances were used to quantify covariate distribution differences between the external, target and deployment cohorts across 20 chronic disease datasets. Points connected by horizontal lines compare distances before and after covariate-shift adjustment. Abbreviations: HF, heart failure; MI, myocardial infarction; CHD, coronary heart disease; COPD, chronic obstructive pulmonary disease; T2DM, type 2 diabetes mellitus; CKD, chronic kidney disease. Source data Extended Data Fig. 2 Weight distributions and normalized effective sample size for density-ratio weighting across all diseases in NHANES and SSACB. Box plots show the normalized density-ratio weights used to align the external and target training datasets with the calibration subset of the deployment population for each disease in a, NHANES and b, SSACB. Disease-specific sample sizes are provided in Supplementary Tables 11 and 32. Weights were normalized to have a mean of 1, and the y-axis is shown on a log10 scale. ESS/n denotes the normalized effective sample size, with higher values indicating more stable weights and smaller effective sample size loss. In each box plot, the centre line denotes the median, box bounds denote the first and third...