提出多维度指标评估生成患者数据的时间一致性，揭示现有方法的隐藏缺陷。
Evaluation metrics for temporal preservation in synthetic longitudinal patient data
- 设计四类指标：边缘分布、协方差、个体轨迹和测量结构，全面评估时间特征保留情况。
- 发现仅边缘相似性好不足以保证时间结构真实，协方差与个体轨迹可能严重失真。
- 适用于医疗生成模型开发者与研究者，尤其关注纵向数据建模质量时必读。
本研究提出一套用于评估合成纵向患者数据中时间保留性的指标，该数据为模拟真实患者随时间重复测量的人工生成数据。所提指标从边缘分布、协方差结构、个体水平轨迹和测量结构四个层面评估合成数据对关键时间特征的还原能力。研究表明，即使边缘分布高度相似，协方差结构和个体轨迹仍可能存在显著偏差。时间保留性受原始数据质量、测量频率及预处理策略（如分箱、变量编码、精度设置）影响。稀疏或高度不规则测量时间的变量难以学习时间依赖关系，导致合成数据与真实数据的相似性下降。单一指标无法充分刻画时间保留性，必须综合多维度评估才能全面判断合成数据质量。该框架阐明了时间结构被保留或破坏的原因，有助于更可靠地评估与改进生成模型，推动构建更具时间真实性的合成纵向患者数据。
原文摘要 · Abstract (English)
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.
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