Evaluation metrics for temporal preservation in synthetic longitudinal patient data
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 46 references that are in the map. First posted on arXiv 11 Feb 2026.
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References · 17
- 2021How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Modelscited by 193
- 2021Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applicationscited by 47
- 2022A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Modelscited by 43
- 2020Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitionscited by 41
- 2024Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challengescited by 24
- 2022PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learningcited by 22
- 2022The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithmscited by 19
- 2024Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcarecited by 17
- 2022A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sourcescited by 13
- 2023Auditing and Generating Synthetic Data with Controllable Trust Trade-offscited by 13
- 2022Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIVcited by 12
- 2017Neural Response Generation with Dynamic Vocabulariescited by 8
5 more
- 2023Synthesize High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Modelcited by 8
- 2023Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Datacited by 8
- 2025A Consensus Privacy Metrics Framework for Synthetic Datacited by 8
- 2023ConSequence: Synthesizing Logically Constrained Sequences for Electronic Health Record Generationcited by 3
- 2023Methods for generating and evaluating synthetic longitudinal patient data: a systematic reviewcited by 1
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