General Demographic Foundation Models for Enhancing Predictive Performance Across Diseases

• URL: http://arxiv.org/abs/2509.07330v1
• Date: Tue, 09 Sep 2025 02:02:27 GMT
• Title: General Demographic Foundation Models for Enhancing Predictive Performance Across Diseases
• Authors: Li-Chin Chen, Ji-Tian Sheu, Yuh-Jue Chuang,
• Abstract summary: This study proposes a General Demographic Pre-trained (GDP) model as a foundational representation framework tailored to age and gender.<n>The model is pre-trained and evaluated using datasets with diverse diseases and population compositions from different geographic regions.
• Score: 0.39508022083907385
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Demographic attributes are universally present in electronic health records and serve as vital predictors in clinical risk stratification and treatment decisions. Despite their significance, these attributes are often relegated to auxiliary roles in model design, with limited attention has been given to learning their representations. This study proposes a General Demographic Pre-trained (GDP) model as a foundational representation framework tailored to age and gender. The model is pre-trained and evaluated using datasets with diverse diseases and population compositions from different geographic regions. The GDP architecture explores combinations of ordering strategies and encoding methods to transform tabular demographic inputs into latent embeddings. Experimental results demonstrate that sequential ordering substantially improves model performance in discrimination, calibration, and the corresponding information gain at each decision tree split, particularly in diseases where age and gender contribute significantly to risk stratification. Even in datasets where demographic attributes hold relatively low predictive value, GDP enhances the representational importance, increasing their influence in downstream gradient boosting models. The findings suggest that foundational models for tabular demographic attributes can generalize across tasks and populations, offering a promising direction for improving predictive performance in healthcare applications.

Related papers

• Investigating the Impact of Histopathological Foundation Models on Regressive Prediction of Homologous Recombination Deficiency [52.50039435394964]
  We systematically evaluate foundation models for regression-based tasks.<n>We extract patch-level features from whole slide images (WSI) using five state-of-the-art foundation models.<n>Models are trained to predict continuous HRD scores based on these extracted features across breast, endometrial, and lung cancer cohorts.
  arXiv  Detail & Related papers  (2026-01-29T14:06:50Z)
• Beyond Traditional Diagnostics: Transforming Patient-Side Information into Predictive Insights with Knowledge Graphs and Prototypes [55.310195121276074]
  We propose a Knowledge graph-enhanced, Prototype-aware, and Interpretable (KPI) framework to predict diseases.<n>It integrates structured and trusted medical knowledge into a unified disease knowledge graph, constructs clinically meaningful disease prototypes, and employs contrastive learning to enhance predictive accuracy.<n>It provides clinically valid explanations that closely align with patient narratives, highlighting its practical value for patient-centered healthcare delivery.
  arXiv  Detail & Related papers  (2025-12-09T05:37:54Z)
• Integrating Genomics into Multimodal EHR Foundation Models [56.31910745104141]
  This paper introduces an innovative EHR foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality.<n>The framework aims to learn complex relationships between clinical data and genetic predispositions.<n>This approach is pivotal for unlocking new insights into disease prediction, proactive health management, risk stratification, and personalized treatment strategies.
  arXiv  Detail & Related papers  (2025-10-24T15:56:40Z)
• Data distribution impacts the performance and generalisability of contrastive learning-based foundation models of electrocardiograms [0.0]
  We present Contrasting by Patient Augmented Electrocardiograms (CAPE) foundation model and pretrain on four cohorts.<n>We assess how cohort demographics, health status, and population diversity influence the downstream performance for prediction tasks.
  arXiv  Detail & Related papers  (2025-09-12T16:01:18Z)
• Exploring Scaling Laws for EHR Foundation Models [17.84205864956449]
  We present the first empirical investigation of scaling laws for EHR foundation models.<n>We identify consistent scaling patterns, including parabolic IsoFLOPs curves and power-law relationships between compute, model parameters, data size, and clinical utility.
  arXiv  Detail & Related papers  (2025-05-29T01:05:11Z)
• Examining Imbalance Effects on Performance and Demographic Fairness of Clinical Language Models [4.390908825243365]
  This study statistically probes the relationship between data imbalance and model performance in ICD code prediction.<n>We analyze imbalances in a standard benchmark data across gender, age, ethnicity, and social determinants of health by state-of-the-art biomedical language models.<n>Our study shows that data imbalance significantly impacts model performance and fairness, but feature similarity to the majority class may be a more critical factor.
  arXiv  Detail & Related papers  (2024-12-23T18:58:11Z)
• Using Backbone Foundation Model for Evaluating Fairness in Chest Radiography Without Demographic Data [2.7436483977171333]
  This study aims to investigate the effectiveness of using the backbone of Foundation Models as an embedding extractor. We propose utilizing these groups in different stages of bias mitigation, including pre-processing, in-processing, and evaluation.
  arXiv  Detail & Related papers  (2024-08-28T20:35:38Z)
• Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models [8.798959872821962]
  This paper outlines an approach in the domain of federated survival analysis, specifically the Cox Proportional Hazards (CoxPH) model. We present an FL approach that employs feature-based clustering to enhance model accuracy across synthetic datasets and real-world applications.
  arXiv  Detail & Related papers  (2024-07-20T18:34:20Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting [64.80538055623842]
  sociodemographic prompting is a technique that steers the output of prompt-based models towards answers that humans with specific sociodemographic profiles would give. We show that sociodemographic information affects model predictions and can be beneficial for improving zero-shot learning in subjective NLP tasks.
  arXiv  Detail & Related papers  (2023-09-13T15:42:06Z)
• IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction [47.999621481852266]
  We propose an interpretable graph learning-based model which interprets the clinical relevance of the input features towards the task. In a clinical scenario, such a model can assist the clinical experts in better decision-making for diagnosis and treatment planning. Our proposed model shows superior performance with respect to compared methods with an increase in an average accuracy of 3.2% for Tadpole, 1.6% for UKBB Gender, and 2% for the UKBB Age prediction task.
  arXiv  Detail & Related papers  (2021-03-29T13:04:02Z)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.