HyperNSDE: Personalized Neural SDEs for Joint Static—Longitudinal Clinical Data Generation

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 the full trajectory dynamics rather than only 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. 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 demonstrate consistent improvements over strong baselines across fidelity, privacy, and utility metrics.