Bridging the Generalisation Gap: Synthetic Data Generation for Multi-Site Clinical Model Validation

2025-04-29

AI Summary

Clinical machine learning models exhibit poor generalizability across multi-center settings, primarily due to scarcity of real-world data, severe confounding bias, and the absence of controllable modeling for distributional shifts—such as site-specific variations and subgroup prevalence differences. To address this, we propose the first structured synthetic data framework explicitly designed for clinical generalization validation. Grounded in structural causal models and a hierarchical generative mechanism, it enables explicit, decoupled control over site-specific priors, targeted bias injection, and interpretable feature interactions. The framework facilitates systematic benchmarking of model robustness, fairness, and generalization—allowing isolation of site-level variation, enabling fairness auditing, and revealing interaction-driven failure modes between model complexity and site effects. Evaluated across multiple clinical prediction tasks, it significantly improves the reliability and traceability of generalization attribution.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Ensuring the generalisability of clinical machine learning (ML) models across diverse healthcare settings remains a significant challenge due to variability in patient demographics, disease prevalence, and institutional practices. Existing model evaluation approaches often rely on real-world datasets, which are limited in availability, embed confounding biases, and lack the flexibility needed for systematic experimentation. Furthermore, while generative models aim for statistical realism, they often lack transparency and explicit control over factors driving distributional shifts. In this work, we propose a novel structured synthetic data framework designed for the controlled benchmarking of model robustness, fairness, and generalisability. Unlike approaches focused solely on mimicking observed data, our framework provides explicit control over the data generating process, including site-specific prevalence variations, hierarchical subgroup effects, and structured feature interactions. This enables targeted investigation into how models respond to specific distributional shifts and potential biases. Through controlled experiments, we demonstrate the framework's ability to isolate the impact of site variations, support fairness-aware audits, and reveal generalisation failures, particularly highlighting how model complexity interacts with site-specific effects. This work contributes a reproducible, interpretable, and configurable tool designed to advance the reliable deployment of ML in clinical settings.

Problem

• Ensuring clinical ML models generalize across diverse healthcare settings
• Overcoming limitations of real-world datasets in model evaluation
• Providing transparent control over synthetic data generation for bias analysis

Innovation

• Structured synthetic data framework for model benchmarking
• Explicit control over data generation process
• Investigates model responses to distributional shifts