arXiv:2607. 21817v1 Announce Type: cross Abstract: Longitudinal studies often collect data at sparse, irregular, and unequally spaced time points.
By Yangsheng Wang, Xiaotian Dai, Haoda Fu, Guifang Fu
The paper introduces a response‑aware patient dynamics model that explicitly captures how respiratory support alters a patient’s short‑term physiological trajectory. By decomposing predicted physiological change into baseline dynamics and support‑associated deviations anchored to room‑air reference, the model updates latent patient states across a forecast horizon. Experiments on ICU cohorts from two institutions show that the model matches baseline trajectory predictions overall and consistently improves performance when physiological states are changing.
By Xiaolei Lu, Shamim Nemati
arXiv:2606. 03689v1 Announce Type: cross Abstract: Survival Analysis (SA) is a statistical framework that models the time span until some event of interest occurs.
By Mariana Vargas Vieyra
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
SepsisLens is a new model for early sepsis warning that preserves the structure of individual physiological signals while predicting risk. It uses observation-aware representations to encode each variable’s dynamics and a shared temporal encoder to maintain variable axes, then composes multi-horizon risk through a StructuredRiskHead that includes variable-level and organ-level components. The model was evaluated on three public ICU cohorts and one private-hospital cohort, achieving strong discrimination and lower alert burden at matched recall on MIMIC-IV, with ablations confirming the design choices.
By Yikun Ou, Wei Li
arXiv:2606. 18281v1 Announce Type: cross Abstract: Conditional average treatment effects (CATEs) are central to treatment decision-making in personalized medicine.
By Daniel Klippert, Sarah Friedrich, Markus Pauly