Evaluation metrics for temporal preservation in synthetic longitudinal patient data
arXiv:2602.10643 · doi:10.1016/j.jbi.2026.105074
Abstract
This study introduces a set of metrics for evaluating temporal preservation in synthetic longitudinal patient data, defined as artificially generated data that mimic real patients' repeated measurements over time. The proposed metrics assess how synthetic data reproduces key temporal characteristics, categorized into marginal, covariance, individual-level and measurement structures. We show that strong marginal-level resemblance may conceal distortions in covariance and disruptions in individual-level trajectories. Temporal preservation is influenced by factors such as original data quality, measurement frequency, and preprocessing strategies, including binning, variable encoding and precision. Variables with sparse or highly irregular measurement times provide limited information for learning temporal dependencies, resulting in reduced resemblance between the synthetic and original data. No single metric adequately captures temporal preservation; instead, a multidimensional evaluation across all characteristics provides a more comprehensive assessment of synthetic data quality. Overall, the proposed metrics clarify how and why temporal structures are preserved or degraded, enabling more reliable evaluation and improvement of generative models and supporting the creation of temporally realistic synthetic longitudinal patient data.
50 pages, 17 figures
References in corpus (6)
- Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges
- A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models
- A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
- Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcare
- Synthesize High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Model
- A Consensus Privacy Metrics Framework for Synthetic Data