发表机构
Inria; Université Paris Cité; Inserm（法国国家信息与自动化研究所; 巴黎西岱大学; 法国国家健康与医学研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出HyperNSDE，一种通过超网络条件化潜在神经随机微分方程的连续时间生成模型，联合建模静态协变量、不规则纵向轨迹和观测时间，提升合成临床数据的真实性与观测时间保真度。
AI 中文摘要
合成患者数据生成是解决医疗机器学习中数据稀缺和隐私约束双重挑战的一种有前景的解决方案。患者级临床数据的逼真合成需要联合建模异质性静态协变量、不规则采样的纵向轨迹以及信息丰富的观测时间——这三个组成部分在实践中紧密耦合，但很少被共同处理。我们提出HyperNSDE，一种连续时间生成模型，通过超网络将潜在神经随机微分方程以静态患者表示为条件，使得基线特征能够在初始条件之外塑造轨迹演化，而无需轨迹编码器，同时随机潜在动力学捕获生成路径中的现实变异性。观测时间通过依赖于潜在状态的强度过程联合建模，并且通过确定性-随机路径分解与非对抗性签名核目标，在不规则随机路径上的训练得到稳定。在模拟和真实临床数据集上的实验显示，观测时间保真度有所提高且性能具有竞争力，而匹配网格分析揭示，预测和相关性度量受观测网格规律性和轨迹平滑度的影响。
英文摘要
Synthetic patient data generation is a promising solution to the dual challenge of data scarcity and privacy constraints in healthcare machine learning. Realistic synthesis of patient-level clinical data requires jointly modeling heterogeneous static covariates, irregularly sampled longitudinal trajectories, and informative observation times - three tightly coupled components in practice yet rarely addressed together. We propose HyperNSDE, a continuous-time generative model that conditions a latent Neural SDE on static patient representations through a hypernetwork, allowing baseline characteristics to shape trajectory evolution beyond the initial condition without requiring a trajectory encoder, while stochastic latent dynamics capture realistic variability in generated paths. Observation times are modeled jointly through a latent-state-dependent intensity process, and training on irregular stochastic paths is stabilized via a deterministic-stochastic path decomposition with a non-adversarial signature-kernel objective. Experiments on simulated and real clinical datasets show improved observation-time fidelity and competitive performance, while matched-grid analyses reveal that forecasting and correlation metrics are affected by observation-grid regularity and trajectory smoothness.
