The study introduces a graph‑enhanced framework for discovering disease trajectories and forecasting temporal endpoints using longitudinal EHR data from MIMIC‑IV v3.1. It constructs patient‑specific motifs from consecutive admissions, applies a time‑aware Transformer to select up to 24 motifs per endpoint, and reports modest improvements in micro‑AUPRC and event‑time MAE, with larger gains for events occurring after 730 days. The authors also explore branching‑motif conditioning and GNN‑based edge repair, finding that motifs mainly provide long‑range timing support rather than superior endpoint discrimination or survival prediction.
arXiv:2610.20512v1 Announce Type: new Abstract: Graph-based analysis of electronic health records (EHRs) yields disease-trajectory catalogs, but structural support does not establish semantic novelty...
GraphBEAT is a temporal graph representation learning framework that bucketizes clinical events from longitudinal electronic health records into time‑ordered graphs, enabling early risk stratification for gastrointestinal cancers. It uses a time‑decay message‑passing mechanism and multi‑head attention pooling to generate patient‑level representations, and was trained on multi‑site diagnosis codes and laboratory tests to predict pancreatic, liver, and intestinal cancer at 12, 24, and 36‑month lead times. Across all experiments, GraphBEAT outperformed baseline graph neural networks and sequence models while requiring far fewer floating‑point operations and parameters.
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."
arXiv:2610.02957v1 Announce Type: new Abstract: World models learn state evolution from trajectories, making access to temporal context a central training requirement. Federated learning can use dist...
arXiv:2604. 27967v2 Announce Type: replace Abstract: Background: We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty.
arXiv:2608. 02663v1 Announce Type: cross Abstract: Accurate ICU mortality prediction requires modeling irregular clinical observations across heterogeneous entity types.
arXiv:2506. 04831v3 Announce Type: replace Abstract: Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions, could support more proactive and personalized care, but requires modeling heterogeneous and longitudinal electronic health record (EHR) data.
arXiv:2602. 00541v2 Announce Type: replace Abstract: Clinical events captured in Electronic Health Records (EHR) are irregularly sampled and may consist of a mixture of discrete events and numerical measurements, such as laboratory values or treatment dosages.
INTERVenE introduces Transformer models that use a knowledge‑based temporal abstraction (KBTA) token stream of named clinical concepts instead of raw measurements, enabling per‑token attributions to resolve directly to clinical concepts. Two variants are offered: an auto‑regressive decoder that generates future abstraction trajectories with step‑wise risk readouts, and a bidirectional encoder that jointly predicts risk and time‑to‑event in a single pass. On 57,078 MIMIC‑IV admissions, the encoder variant outperforms neural baselines with a support‑weighted AUPRC of 0.672 and AUROC of 0.901, while the decoder provides complementary token‑level risk trajectories.
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.
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...
arXiv:2607. 18270v1 Announce Type: new Abstract: While Electronic Health Records (EHRs) offer a wealth of clinical data, effectively augmenting a patient's records with heterogeneous external knowledge to predict the patient's clinical risk remains a significant challenge.