General Demographic Pre-trained Models for Enhancing Predictive Performance Across Diseases and Population

Abstract:Foundation models for healthcare require balancing robust generalization across heterogeneous clinical populations and disease settings with the architectural simplicity needed for deployment. We present a pre-trained model focused on demographic attributes that enhances feature utility across medical domains in a plug-and-play fashion. We introduce the General Demographic Pre-trained (GDP) model, designed to extract intrinsic representations of patient status based on age and sex, the two most ubiquitous clinical features. The composition of GDP was optimized by investigating various encoding methods and visit-reordering schemes. The model was pre-trained and transferability was validated by embedding the learned representations into diverse disease and geographic cohorts characterized by distinct demographic profiles. The optimal model configuration was subsequently validated against top-performing tabular foundation models (TabPFN, TabICL, and TabFM). Our findings demonstrate that concatenating GDP-derived embeddings with raw residual features consistently enhances predictive performance across classification tasks while elevating the relative importance of demographic attributes. The embedding transformation provides superior representational separability compared to the original data distribution, yielding competitive discrimination performance across metrics against all three general-purpose foundation models and tree-based algorithm. GDP has successfully served the purpose of a foundation model, which produce enriched representations that amplify the predictive insight of these features beyond their raw form. The generated embeddings can be directly concatenated with residual features, serving as an enhancement layer that maintains full compatibility with standard tabular classifiers.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2509.07330 [cs.LG]

(or arXiv:2509.07330v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2509.07330 Focus to learn more arXiv-issued DOI via DataCite

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