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arXiv Machine Learning By Saeed Farzi, Sofia Michopoulou, Christopher Kipps, Rahman Attar

Intervention-Conditioned Counterfactual Human Digital Twin Generator

Read the original on arXiv Machine Learning →

The paper introduces CHDT, an intervention‑conditioned Human Digital Twin generator designed to model multimodal longitudinal patient trajectories. It explicitly accounts for irregular visit intervals and cross‑mediator dependencies, which are often neglected in existing models. Experiments on ADNI and ALPACA datasets demonstrate that CHDT achieves higher long‑horizon patient‑state fidelity compared to baseline methods, with irregular‑time conditioning providing the main benefit and cross‑mediator refinement adding further improvement.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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