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.
arXiv:2606. 09671v1 Announce Type: cross Abstract: Alzheimer's disease (AD) progression is highly heterogeneous and is typically observed through sparse and irregular longitudinal data, posing challenges for prediction and personalised monitoring.
arXiv:2607. 00431v1 Announce Type: new Abstract: Forecasting models for health-signal digital twins must preserve the oscillatory, frequency, phase, and state-transition dynamics of physiological signals, yet the pointwise metrics used to benchmark them cannot detect when these fundamental properties are lost.
Alzheimer's disease (AD) progression is highly heterogeneous and is typically observed through sparse and irregular longitudinal data, posing challenges for prediction and personalised monitoring. Existing machine learning approaches have improved AD prediction using multimodal data, yet often focus on static classification or cohort-level risk estimation, providing limited support for subject-specific modelling and uncertainty-aware reasoning.
arXiv:2606. 27334v1 Announce Type: new Abstract: Digital twins have emerged as a promising paradigm for personalized healthcare, enabling modeling of individual behavior and health trajectories.
arXiv:2301. 07210v5 Announce Type: replace-cross Abstract: Digital twins are simulation-based models designed to predict how a real-world process will evolve in response to interventions.
The paper presents a reliability‑aware benchmarking framework for short‑term glycemic risk prediction, using 5,503,441 prediction anchors from 736 type‑1 diabetes patients. Three neural models—a Hybrid LSTM‑Transformer, a Multimodal Digital Twin, and a State‑Updating Digital Twin—were evaluated on 30‑, 60‑, and 120‑minute horizons for hypoglycemia and hyperglycemia, with identical patient‑level validation. While no significant differences in predictive accuracy were found, the models displayed distinct reliability traits: the Multimodal Digital Twin had the lowest Brier score, the State‑Updating Digital Twin the lowest expected calibration error, and the Hybrid LSTM‑Transformer the strongest explanation faithfulness.
arXiv:2606. 05797v1 Announce Type: new Abstract: Longitudinal treatment decisions require predicting potential outcomes under future treatment sequences in the presence of time-varying confounding, heterogeneous patient dynamics, and limited domain-specific data.
The paper investigates how learned multimodal representations of high‑dimensional health data can fail to preserve causal information, even when trained to reconstruct the data. It establishes conditions under which a representation is sufficient for causal identification, shows that interventional mediation requires stricter preservation than counterfactual comparison, and proves that reconstruction‑based objectives cannot certify sufficiency. The authors decompose the error of cross‑fitted doubly robust estimators into a first‑order representation bias and a second‑order nuisance‑estimation remainder, demonstrating that coverage can deteriorate with larger samples if the representation is insufficient. They propose a practical workflow that combines modality‑specific variational autoencoders, cross‑fitted doubly robust estimation, locked phenotypes, selective pathway analysis, portable representations, and diagnostics for representation insufficiency, exposure leakage, and sensitivity, and validate it in simulations where a rare confounder’s omission leads to severely under‑covered indirect‑effect intervals.
arXiv:2608. 12592v1 Announce Type: new Abstract: Continuous physiological time series underpin modern clinical monitoring, yet many of the most informative signals are invasive, expensive, or simply unavailable for a given patient.
arXiv:2609.36144v1 Announce Type: new Abstract: Electronic health records provide irregular observations of latent patient states that evolve continuously over time. Recent autoregressive models cond...
arXiv:2607. 21403v1 Announce Type: new Abstract: Mobile-health interventions increasingly use online learning and decision making algorithms to personalize when to nudge users toward healthier behavior, but a poorly designed algorithm can burden and disengage participants.
arXiv:2608. 14969v1 Announce Type: new Abstract: We present a physiology-informed digital twin of the human liver designed for longitudinal simulation of liver function and early-stage disease progression.