Generative models for synthetic EMRs match marginal distributions but fail to preserve subgroup structure, effect estimates, and dependency structure simultaneously on the PRIME-CVD cohort.
Ck4gen: A knowledge distillation framework for generating high-utility synthetic survival datasets in healthcare.arXiv preprint arXiv:2410.16872, 2024
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Fine-tuning an LLM on text-encoded clinical covariates to match Cox survival predictions yields competitive held-out discrimination and calibration on three datasets, with t-SNE showing smooth risk gradients in latent space.
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Synthetic but Not Realistic: The Evaluation Challenge in Generative Modelling for Structured Electronic Medical Records
Generative models for synthetic EMRs match marginal distributions but fail to preserve subgroup structure, effect estimates, and dependency structure simultaneously on the PRIME-CVD cohort.
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From Hazard Functions to Language Space: Cox-Supervised Distillation of Survival Risk into a Large Language Model
Fine-tuning an LLM on text-encoded clinical covariates to match Cox survival predictions yields competitive held-out discrimination and calibration on three datasets, with t-SNE showing smooth risk gradients in latent space.