Article

Synthetic-data augmented calibration for expert-informed rare disease models

2026-05-20

Abstract excerpt

Clinical data for rare diseases are sparse, noisy, and heterogeneous, complicating calibration of ordinary differential equation (ODE) models. Thus, we introduce a noise-robust calibration in latent space that combines expertderived ODEs with learned latent representations. Our approach leverages synthetic ODE trajectories, augmenting our scarce observations to train a model-specific autoencoder representation and...

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Literature Corpus work: b301567e-4035-5ace-8921-312a737ecbcf
DOI: 10.64898/2026.05.18.725833

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