arXiv:2610.31968v1 Announce Type: new Abstract: Accurate ambulance travel time estimation is critical for emergency medical dispatch, yet existing decision-support practices often rely on static look...
HealthMamba is an uncertainty‑aware spatiotemporal graph state space model designed to predict healthcare facility visits more accurately and reliably. It integrates a Unified Spatiotemporal Context Encoder, a novel GraphMamba module for hierarchical modeling, and a comprehensive uncertainty quantification component. Evaluated on large‑scale datasets from California, New York, Texas, and Florida, it improves prediction accuracy by about 6.0% and uncertainty quantification by 3.5% over state‑of‑the‑art baselines.
This study analyzes 2,850,575 ambulance attendances in Victoria, Australia, to quantify ambulance ramping—the delay between hospital arrival and patient handover—across 59 hospitals and 79 local government areas from January 2020 to March 2024. It finds that cumulative ambulance hours lost (AHL) totalled 1,491,127 hours, with ten hospitals accounting for 57.8 % of lost hours. Ramping duration increases with the number of ambulances arriving at the same hospital in the preceding hour and shows a moderate correlation with hourly demand two to four hours later.
arXiv:2512.08281v2 Announce Type: replace-cross Abstract: Accurate and reliable aircraft landing time prediction is essential for effective resource allocation in air traffic management. However, the...
The paper introduces egRUE, an explainable uncertainty estimation method that merges uncertainty quantification with feature‑level explanations for medical AI predictions. egRUE incorporates prediction explanations into its uncertainty calculation and decomposes uncertainty into contributions from individual features. Experiments and a user study with medical experts show that egRUE improves reliability, interpretability, and calibrated trust compared to existing methods.
arXiv:2511. 23276v2 Announce Type: replace Abstract: Effective HFMD surveillance requires forecasts capturing both time-series patterns and contextual drivers such as school calendars, weather, and policy or surveillance reports.
arXiv:2607. 17758v1 Announce Type: new Abstract: Managing massive crowds during infrequent special events requires reliable real-time pedestrian-flow forecasting to ensure public safety and operational efficiency.
arXiv:2512. 15067v4 Announce Type: replace-cross Abstract: The rapid growth in wireless infrastructure has increased the need to accurately estimate and forecast electromagnetic field (EMF) levels to ensure ongoing compliance, assess potential health impacts, and support efficient network planning.
The paper presents a machine‑learning approach (ML_CP) that automatically learns patterns and rules for complex event prediction, reducing reliance on manual rule creation. It incorporates sensitivity analysis to assess how output varies with each input and uses conformal prediction to generate uncertainty‑aware prediction intervals. Experiments on binary, multi‑level classification, and regression tasks show promising results for safety‑critical embedded systems.
arXiv:2606. 15240v1 Announce Type: new Abstract: Vessel trajectory prediction is important for intelligent shipping, maritime surveillance, and navigation safety.
UTP-Bench is a new benchmark for uncertainty-aware travel planning that evaluates large language models on their ability to generate robust itineraries under real-world stochastic conditions. The dataset covers 504 Indian cities, incorporating attractions, restaurants, accommodations, and multi-modal transportation networks, and includes empirical delay distributions and crowd-density patterns to simulate realistic disruptions. Three new metrics—Buffer Adequacy Score, Crowd-Aware Timing Score, and Transport Delay Absorption Score—measure how well generated plans maintain robustness against transit delays and crowd variability, revealing significant gaps between state-of-the-art LLMs and human-authored itineraries.
arXiv:2606. 19569v1 Announce Type: new Abstract: Uncertainty quantification (UQ) is essential for reliable decision-making in safety-critical applications in probabilistic machine learning.