TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation

Abstract: Synthetic Electronic Health Record (EHR) time-series generation is crucial for advancing clinical machine learning models, as it helps address data scarcity by providing more training data. However, most existing approaches focus primarily on replicating statistical distributions and temporal dependencies of real-world data. We argue that fidelity to observed data alone does not guarantee better model performance, as common patterns may dominate, limiting the representation of rare but important conditions. This highlights the need for generate synthetic samples to improve performance of specific clinical models to fulfill their target outcomes. To address this, we propose TarDiff, a novel target-oriented diffusion framework that integrates task-specific influence guidance into the synthetic data generation process. Unlike conventional approaches that mimic training data distributions, TarDiff optimizes synthetic samples by quantifying their expected contribution to improving downstream model performance through influence functions. Specifically, we measure the reduction in task-specific loss induced by synthetic samples and embed this influence gradient into the reverse diffusion process, thereby steering the generation towards utility-optimized data. Evaluated on six publicly available EHR datasets, TarDiff achieves state-of-the-art performance, outperforming existing methods by up to 20.4% in AUPRC and 18.4% in AUROC. Our results demonstrate that TarDiff not only preserves temporal fidelity but also enhances downstream model performance, offering a robust solution to data scarcity and class imbalance in healthcare analytics.

TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation

The study presented by Bowen Deng, Chang Xu, Hao Li, Yuhao Huang, Min Hou, and Jiang Bian addresses a critical gap in the landscape of clinical machine learning—sufficient and efficient synthesis of Electronic Health Record (EHR) time-series data. The primary focus of the paper is the introduction and evaluation of TarDiff, a novel target-oriented diffusion model. The model embeds the concept of influence functions into the synthetic generation process to optimize synthetic samples for improving downstream models' performance on specific clinical targets.

Introduction to EHR Synthetic Data Generation

Electronic Health Records capture comprehensive patient data, including demographics, vitals, and diagnostics, serving as a central component in aiding clinical research and decision-making. However, the acquisition and use of EHRs are fraught with challenges, particularly due to privacy regulations, data incompleteness, and class imbalance in diseases, especially rare conditions. To mitigate these issues, there is a strong imperative to use synthetic data to augment learning models while maintaining privacy compliance and data richness.

Most traditional approaches in synthetic data generation attempt to replicate the statistical properties of the dataset. Commonly employed generative models such as GANs, VAEs, and their diffusion-based counterparts often focus on mimicking the empirical data distribution without considering the enhancement in the predictive capabilities of downstream clinical tasks.

Methodology: Diffusion Process with Influence Guidance

In contrast, TarDiff adopts a distinct direction, integrating an influence mechanism within a diffusion framework. The influence function measures the expected reduction in task-specific loss when synthetic samples are added to the training set. By embedding this influence gradient into the reverse diffusion process, the framework strategically generates synthetic data tailored to enhance model performance rather than merely replicating real-data characteristics. This means that TarDiff inherently considers the clinical utility aspect by steering the generation process towards samples that improve task outcomes, catering particularly to class imbalances where rare disease detection is crucial.

Experimental Validation

The TarDiff model's efficacy is empirically validated across six publicly accessible EHR datasets. The results demonstrate state-of-the-art performance, showing improvements of up to 20.4% in AUPRC and 18.4% in AUROC over other methods. Such findings underscore the framework's ability not only to preserve temporal fidelity but also to significantly enhance downstream prediction tasks.

The experiment setup includes assessing the model's performance by comparing it to other synthetic data generation techniques, where TarDiff consistently demonstrates superior performance metrics. The experiments span over common clinical predictive tasks, including mortality and ICU stay predictions, illustrating the general applicability and robustness of the proposed approach.

Implications and Future Directions

Practically, the TarDiff model addresses the pressing issue of class imbalance and data scarcity in EHR datasets, which is pivotal for developing keen insights into rare pathologies and optimizing patient-specific treatments. Theoretically, this approach contributes to the discourse on utility-guided data synthesis, where models are not only data-driven but also outcome-oriented, leveraging influence functions to quantify utility at a more granular level during the data generation process.

Future research could explore expanding this framework's applications beyond healthcare—especially in domains with similar data constraints like finance or public policy. Additionally, the integration of more complex influence functions and exploring multi-task utility optimization could further augment the framework's adaptability and effectiveness across assorted data contexts.

In summary, TarDiff exemplifies a structured, methodical approach to synthetic EHR data generation that prioritizes clinical impact, thereby marking a pertinent advancement in the optimization of healthcare machine learning models.