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Patient Digital Twin Updates Forecasts Without Retraining
US & West ·
ICare-DT, a Transformer-based twin, updates a latent patient state hourly from new measurements without retraining. Trained on 140,473 eICU-CRD stays, its later forecasts beat earlier ones in all 12 settings, with gains confirmed on a second database and traced to the patient's own measurements.
Why it matters
Demonstrates continuous forecast updating as new data arrives and robustness when transferred to another database, a central question for any predictive model of large systems.
Next-generation wireless networks are increasingly large-scale, heterogeneous, and dynamic, posing significant challenges to network modeling and control. Digital twins offer a promising solution, but existing approaches typically rely on static models and decouple twin construction from network…
Cardiac digital twins (CDTs) provide a computational platform for personalized cardiac modeling, but their predictive behavior depends strongly on the selected ionic model and the representation of transmural electrophysiological heterogeneity.