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.
By Haochen Zhang, Jiaheng Guo, Yu-Chao Huang, Nicholas Knoz, Tianlong Chen
arXiv:2606. 15637v1 Announce Type: new Abstract: A digital twin (DT) of a patient-specific heart offers significant potential in personalized medicine.
By Sumeet Vadhavkar, Xiajun Jiang, Yubo Ye, Maryam Toloubidokhti, Linwei Wang
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. Conditional generation offers a remedy: an absent signal can be synthesized from co-recorded signals and routine clinical variables.
CAMOS is a coupled oscillatory state‑space model designed for multimodal clinical time‑series that are irregularly sampled and often incomplete. It addresses a representational limitation of linear state‑space models by allowing the transition operator to depend on which modalities are present, using a bank of second‑order oscillators coupled through a gated matrix. On the ADNI dataset, CAMOS outperforms both uncoupled oscillatory models and clinical fusion models in same‑visit staging, landmark prediction, and longitudinal forecasting, and uniquely avoids collapsing to the majority class when transferred zero‑shot to OASIS‑3.
By Maxx Richard Rahman, Mostafa Hammouda, Wolfgang Maass
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...
By Sizhe Ma, Katherine A. Flanigan, Mario Berg\'es
Deep learning models for electrocardiogram (ECG) classification often suffer from significant performance degradation when deployed in unseen domains due to shifts in acquisition devices and patient p...