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Intervention-Conditioned Counterfactual Human Digital Twin Generator — arXiv:2610.40664

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40664
License (as archived): CC0-1.0
Archived: 2026-11-03T08:12:43.533Z
SHA-256 of the archived text: 52537383637a64fc9e799f2dfcdde6da473c877a8fba6e777d5d9a682d74afd7
Stable URL of this page: https://metraipolis.com/documento/dc_95d42d052d34

How to cite

Intervention-Conditioned Counterfactual Human Digital Twin Generator — arXiv:2610.40664 — arXiv (en), https://arxiv.org/abs/2610.40664 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_95d42d052d34 (Metraipolis archive dc_95d42d052d34, sha256 52537383637a64fc9e799f2dfcdde6da473c877a8fba6e777d5d9a682d74afd7)

BibTeX

@misc{dc95d42d052d34,
  title = {Intervention-Conditioned Counterfactual Human Digital Twin Generator — arXiv:2610.40664},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40664},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_95d42d052d34; sha256 52537383637a64fc9e799f2dfcdde6da473c877a8fba6e777d5d9a682d74afd7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40664. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Quantitative Methods (q-bio.QM) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.40664 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T08:02:38.960Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).