arXiv:2606. 11264v1 Announce Type: cross Abstract: Health digital twins (HDTs) promise patient-specific modeling and decision support but current approaches remain structurally fragmented: monolithic models that address a single organ or task lack cross-scale fidelity, while system-level twins lack generalizable architectural frameworks.
arXiv:2608. 02135v1 Announce Type: cross Abstract: Cardiovascular digital twins aim to create patient-specific computational models that evolve with clinical data to support diagnosis, prognosis, and therapy optimisation.
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: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.
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. 25185v1 Announce Type: new Abstract: Accurately predicting the spatiotemporal evolution of amyloid-$\beta$ and tau proteins at the individual level is critical for improving the diagnosis and treatment of Alzheimer's disease.
arXiv:2609.40071v1 Announce Type: cross Abstract: Digital twins increasingly support downstream analytical tasks that depend on time-series data, motivating interest in time-series foundation models...
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:2606. 18154v1 Announce Type: new Abstract: Building personalized cardiac electrophysiology (EP) digital twins requires identifying the appropriate model structure for each patient, not merely fitting parameters.
The paper introduces a four‑layer architecture for Cognitive Digital Twins (CDTs) that extends traditional Digital Twin systems by embedding cognitive capabilities. It outlines a self‑evolving closed loop across physical, digital‑twin, cognitive, and task layers, enabling task‑specific cognitive models and decision generation that are refined through operational feedback. Two operation modes—user‑request‑driven and self‑driven cognition—are described, along with key enabling mechanisms and a lightweight simulation demonstrating closed‑loop feasibility and efficiency gains.
As Digital Twin (DT) systems evolve beyond state synchronization toward task-oriented and knowledge-driven operation, Cognitive Digital Twins (CDTs) have emerged as an extension that incorporates cogn...
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.
The paper introduces TNFL, a trust‑network‑based federated learning framework designed for multi‑center aging clock prediction. TNFL propagates models along directed trust relations without centralized aggregation, combining an age‑aware mixture‑of‑experts model with generative replay to mitigate forgetting and drift. Experiments on multiple molecular datasets demonstrate effective aging‑clock prediction with limited local data, interpretable age‑dependent patterns, and stable performance across interaction orders, while revealing coordinated higher‑order protein subnetworks linked to aging.