SynRL: Aligning Synthetic Clinical Trial Data with Human-preferred Clinical Endpoints Using Reinforcement Learning

2024-11-11

arXiv.org

AI Summary

This work addresses the challenge of balancing privacy preservation and analytical utility in clinical trial data sharing. We propose the first reinforcement learning–based synthetic data generation framework utilizing Proximal Policy Optimization (PPO). Our method introduces a learnable, differentiable data utility evaluator that directly embeds user-specified clinical endpoints and downstream analysis objectives into the generative process, enabling end-to-end alignment between synthesis and clinical goals—thereby departing from conventional post-hoc evaluation paradigms. The framework supports multi-source data adaptation, modular integration of diverse generative models, and built-in privacy safeguards. Extensive experiments across four real-world clinical trial datasets demonstrate that our synthetic data significantly outperforms baselines in statistical fidelity, consistency with clinical endpoint prediction, and downstream task performance, while substantially reducing re-identification risk.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Privacy
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Each year, hundreds of clinical trials are conducted to evaluate new medical interventions, but sharing patient records from these trials with other institutions can be challenging due to privacy concerns and federal regulations. To help mitigate privacy concerns, researchers have proposed methods for generating synthetic patient data. However, existing approaches for generating synthetic clinical trial data disregard the usage requirements of these data, including maintaining specific properties of clinical outcomes, and only use post hoc assessments that are not coupled with the data generation process. In this paper, we propose SynRL which leverages reinforcement learning to improve the performance of patient data generators by customizing the generated data to meet the user-specified requirements for synthetic data outcomes and endpoints. Our method includes a data value critic function to evaluate the quality of the generated data and uses reinforcement learning to align the data generator with the users' needs based on the critic's feedback. We performed experiments on four clinical trial datasets and demonstrated the advantages of SynRL in improving the quality of the generated synthetic data while keeping the privacy risks low. We also show that SynRL can be utilized as a general framework that can customize data generation of multiple types of synthetic data generators. Our code is available at https://anonymous.4open.science/r/SynRL-DB0F/.

Problem

• Aligning synthetic clinical trial data
• Improving data generator performance
• Customizing data to user-specified endpoints

Innovation

• Reinforcement Learning data alignment
• Custom synthetic clinical data
• Data value critic function