The paper introduces Curriculum‑Aware Interpolate‑then‑Refine (CAIR), a two‑stage framework for imputing physiological time‑series data. CAIR first learns a coarse base curve with a bidirectional‑GRU interpolator and then refines it through three Transformer passes, trained under a random‑gap curriculum that mimics realistic missingness. Evaluations on continuous glucose monitoring and arterial pressure datasets show CAIR outperforms all baselines across MCAR, MAR, and NMAR mechanisms, especially for long gaps and value‑dependent dropout, while also preserving clinically relevant burden metrics.
By Yu-Chao Huang, Haochen Zhang, Nicholas Konz, Tianlong Chen
arXiv:2606. 06328v1 Announce Type: new Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire respiratory channel is unavailable during overnight monitoring.
By Ziwen Kan, Wugeng Zheng, Tianlong Chen, Song Wang
arXiv:2608.21941v1 Announce Type: new
Abstract: Accurate assessment of patients in intensive care units (ICUs) is essential for timely clinical intervention and improved patient outcomes. Multimodal...
By Yixin Yang, Yueyang Sun, Weichen Liu, Xianbing Zhao, Sicen Liu
Accurate assessment of patients in intensive care units (ICUs) is essential for timely clinical intervention and improved patient outcomes. Multimodal electronic health records (EHRs), including struc...
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:2607. 23412v1 Announce Type: new Abstract: Electrocardiograms (ECGs) are widely used for cardiovascular risk prediction, yet models often fail to transfer across hospitals because of protocol, population, and measurement differences.
By Jie Lin, Weijie Sun, Sunil V. Kalmady, Anita Khalafbeigi, Abram Hindle, Padma Kaul, Russell Greiner
PGP-Clinical-TimeKAN is a trajectory-first framework for joint probabilistic forecasting of multivariate physiological data, combining missingness-aware temporal encoders, a soft organ-system prior, patient-specific relations, nonlinear Kolmogorov‑Arnold messages, and a low‑rank multivariate Student‑t head. Evaluated on a MIMIC‑IV cohort of 6,882 patients, it achieves the second‑lowest normalized MAE and the lowest RMSE among 13 models, while providing calibrated probabilistic forecasts with empirical coverage at 50%, 80%, and 95% intervals. Ablation studies show that relational structure is critical for performance, and increasing covariance rank improves likelihood but not point accuracy.
whyItMatters":"The model demonstrates that joint trajectory forecasting can yield highly accurate, calibrated predictions of physiological trajectories, offering a potentially inspectable intermediate task for clinical deterioration prediction."
By Weizhi Nie, Rihao Chang, Weijie Wang, Yuting Su
NeoTriFuse is a reliability‑aware multimodal fusion framework designed to predict neonatal mortality risk from bedside monitoring data that suffers from extreme class imbalance, heterogeneous risk factors, multi‑scale temporal dynamics, and significant missingness. The method treats missing data as an explicit reliability signal, dynamically adjusting modality contributions during fusion through gating mechanisms that incorporate static perinatal variables, local‑global temporal encoders, and patient‑level statistical summaries. NeoTriFuse achieves competitive performance (F1 ≈ 0.674, AUROC ≈ 0.945) and ablation studies show that its temporal architecture and patient‑level summary branch are key contributors, with reliability‑aware gating further improving threshold‑dependent metrics under heterogeneous observation completeness.
By Jiyuan Tian, Qincheng Shen, Ye Lin, Yu Gao, Haohui Lu
arXiv:2608.29301v1 Announce Type: new
Abstract: Predicting future organ dysfunction in Intensive Care Unit (ICU) patients is critical for early clinical intervention, yet existing machine learning ap...
By Razan Albouq, Asra Aslam
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.
arXiv:2607. 21922v1 Announce Type: new Abstract: Clinical irregular multivariate time series are shaped not only by physiological dynamics but also by the measurement process that determines when and what to observe.
By Mingyi Ma, Qingxiong Tan
Time-series foundation models have demonstrated strong cross-domain transfer, yet their common architectural assumptions remain poorly aligned with wearable physiological signals, which are multichann...