CDMT-EHR: A Continuous-Time Diffusion Framework for Generating Mixed-Type Time-Series Electronic Health Records
arXiv:2603.23719v1 Announce Type: new Abstract: Electronic health records (EHRs) are invaluable for clinical research, yet privacy concerns severely restrict data sharing. Synthetic data generation offers a promising solution, but EHRs present unique challenges: they contain both numerical and categorical features that evolve over time. While diffusion models have demonstrated strong performance in EHR synthesis, existing approaches predominantly rely on discrete-time formulations, which suffer from finite-step approximation errors and coupled training-sampling step counts. We propose a continuous-time diffusion framework for generating mixed-type time-series EHRs with three contributions: (1) continuous-time diffusion with a bidirectional gated recurrent unit backbone for capturing temporal dependencies, (2) unified Gaussian diffusion via learnable continuous embeddings for categorical variables, enabling joint cross-feature modeling, and (3) a factorized learnable noise schedule tha
arXiv:2603.23719v1 Announce Type: new Abstract: Electronic health records (EHRs) are invaluable for clinical research, yet privacy concerns severely restrict data sharing. Synthetic data generation offers a promising solution, but EHRs present unique challenges: they contain both numerical and categorical features that evolve over time. While diffusion models have demonstrated strong performance in EHR synthesis, existing approaches predominantly rely on discrete-time formulations, which suffer from finite-step approximation errors and coupled training-sampling step counts. We propose a continuous-time diffusion framework for generating mixed-type time-series EHRs with three contributions: (1) continuous-time diffusion with a bidirectional gated recurrent unit backbone for capturing temporal dependencies, (2) unified Gaussian diffusion via learnable continuous embeddings for categorical variables, enabling joint cross-feature modeling, and (3) a factorized learnable noise schedule that adapts to per-feature-per-timestep learning difficulties. Experiments on two large-scale intensive care unit datasets demonstrate that our method outperforms existing approaches in downstream task performance, distribution fidelity, and discriminability, while requiring only 50 sampling steps compared to 1,000 for baseline methods. Classifier-free guidance further enables effective conditional generation for class-imbalanced clinical scenarios.
Executive Summary
The proposed CDMT-EHR framework leverages a continuous-time diffusion model to generate mixed-type time-series electronic health records (EHRs), addressing the unique challenges posed by EHRs. By introducing a bidirectional gated recurrent unit backbone, unified Gaussian diffusion, and a factorized learnable noise schedule, the authors demonstrate significant improvements in downstream task performance, distribution fidelity, and discriminability compared to existing discrete-time formulations. The framework's capabilities in conditional generation and class-imbalanced scenarios also showcase its potential in clinical applications. With a 50-step sampling process, CDMT-EHR offers a promising solution to the data sharing and synthetic data generation challenges in EHRs, warranting further exploration and evaluation.
Key Points
- ▸ CDMT-EHR employs a continuous-time diffusion framework for generating mixed-type time-series EHRs.
- ▸ The framework utilizes a bidirectional gated recurrent unit backbone and unified Gaussian diffusion for capturing temporal dependencies and joint cross-feature modeling.
- ▸ CDMT-EHR's factorized learnable noise schedule adapts to per-feature-per-timestep learning difficulties, enabling efficient sampling.
Merits
Strength in Temporal Dependency Modeling
CDMT-EHR's bidirectional gated recurrent unit backbone effectively captures temporal dependencies in EHRs, contributing to improved downstream task performance.
Unified Gaussian Diffusion for Categorical Variables
The unified Gaussian diffusion approach enables joint cross-feature modeling, effectively handling mixed-type EHRs and enhancing overall performance.
Efficient Sampling through Factorized Learnable Noise Schedule
The factorized learnable noise schedule allows for efficient sampling with only 50 steps, significantly reducing the computational requirements compared to existing discrete-time formulations.
Demerits
Potential Overfitting Risks
CDMT-EHR's complex architecture may lead to overfitting, particularly with the introduction of learnable continuous embeddings for categorical variables. Careful regularization and validation are essential to mitigate this risk.
Limited Generalizability to Diverse Clinical Settings
The framework's performance on two large-scale intensive care unit datasets may not directly generalize to diverse clinical settings, such as general hospital or outpatient settings. Further evaluation and adaptation are required for broader applicability.
Expert Commentary
The CDMT-EHR framework presents a significant advancement in the field of synthetic EHR generation, capitalizing on the strengths of continuous-time diffusion models. While the proposed framework demonstrates impressive performance and efficiency, careful consideration of potential limitations and risks is essential. Moreover, the framework's adaptability to diverse clinical settings and its scalability to large-scale datasets warrant further investigation. The implications of CDMT-EHR extend beyond clinical applications, influencing policy decisions and shaping the future of EHR data sharing and synthetic data generation.
Recommendations
- ✓ Further evaluation and adaptation of CDMT-EHR for diverse clinical settings and large-scale datasets are necessary to ensure its broad applicability.
- ✓ Investigating the framework's potential for real-world clinical applications, such as developing synthetic EHRs for training machine learning models, is recommended.
Sources
Original: arXiv - cs.LG
