{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.07383",
  "author_total": 2,
  "authors": [
    {
      "name": "Perrine Chassat"
    },
    {
      "name": "Agathe Guilloux"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0c0cxtsdzsb8r.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.07383",
  "fulltext_url": null,
  "id": "0c0cxtsdzsb8r",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.07383",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Machine Learning in Healthcare"
    ]
  ],
  "openalex": "W7220990075",
  "pdf_url": "https://arxiv.org/pdf/2610.07383",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0c0cxtsdzsb8r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:32Z",
      "native_id": "2610.07383",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.07383"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "HyperNSDE: Personalized Neural SDEs for Joint Static-Longitudinal Clinical Data Generation",
  "title_text": "HyperNSDE: Personalized Neural SDEs for Joint Static-Longitudinal Clinical Data Generation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:32Z",
  "url": "https://research.draconitic.com/paper/0c0cxtsdzsb8r",
  "venue": "arXiv",
  "versions": []
}