{
  "abstract": "Chronic diseases evolve through long, heterogeneous trajectories shaped by interacting mediators, confounders, and treatment histories. Estimating long-term patient trajectories under alternative interventions is essential for in silico clinical trial design. However, most existing models overlook irregular visit intervals and cross-mediator dependencies, both of which are crucial for reliable long-term longitudinal prediction. Moreover, these models typically focus on outcome prediction rather than generating complete patient-state trajectories. To address these challenges, we propose CHDT, an intervention-conditioned Human Digital Twin generator under standard identification assumptions for multimodal longitudinal trajectory estimation. Its main empirical advantage arises from explicit modeling of irregular visit intervals, while cross-mediator refinement provides additional gains in selected intervention-conditioned settings. Across real-world ADNI and simulator-generated ALPACA cohorts, CHDT shows improved long-horizon patient-state fidelity relative to competing baselines. Ablation results indicate that irregular-time conditioning provides the primary empirical gain, and cross-mediator refinement provides additional improvements.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.40664",
  "author_total": 4,
  "authors": [
    {
      "name": "Saeed Farzi"
    },
    {
      "name": "Sofia Michopoulou"
    },
    {
      "name": "Christopher Kipps"
    },
    {
      "name": "Rahman Attar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0cmaa7pcss90c.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.40664",
  "fulltext_url": null,
  "id": "0cmaa7pcss90c",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.40664",
  "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",
      "q-bio.QM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.40664",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0cmaa7pcss90c.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:21Z",
      "native_id": "2610.40664",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.40664"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Intervention-Conditioned Counterfactual Human Digital Twin Generator",
  "title_text": "Intervention-Conditioned Counterfactual Human Digital Twin Generator",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:53Z",
  "url": "https://research.draconitic.com/paper/0cmaa7pcss90c",
  "venue": "arXiv",
  "versions": []
}