arXiv:2606. 16721v1 Announce Type: new Abstract: Medical diagnosis and treatment are dynamic processes in which patient states evolve over time and clinical interventions alter future outcomes.
arXiv:2610.17059v1 Announce Type: new Abstract: Clinical decisions depend on anticipating how a patient's condition will evolve as treatment is adjusted and new evidence becomes available. Existing m...
arXiv:2607. 08793v1 Announce Type: cross Abstract: Sepsis is a leading cause of mortality, yet optimal treatment policies remain contested.
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.
arXiv:2606. 04632v1 Announce Type: new Abstract: Mechanical ventilation for Acute Respiratory Distress Syndrome (ARDS) requires balancing competing physiological goals, including oxygenation, lung protection, and acid-base homeostasis.
NOAH is a generative transformer that models the entire multimodal patient journey by integrating bidirectional time and a variational latent space to capture continuous, stochastic clinical trajectories. Trained on over 559 million events from 431,000 hospital visits, it processes medical images, time‑series, numeric signals, categorical events, and both structured and unstructured records. The model supports autoregressive forecasting, zero‑shot classification, and counterfactual simulations, yielding strong predictive performance across 15 ICD chapters, 29 comorbidities, and time‑to‑event outcomes.
arXiv:2510. 15127v3 Announce Type: replace-cross Abstract: Identifying the effects of mechanical ventilation (MV) protocols in critical care requires analyzing data from heterogeneous patient-ventilator systems in the clinical decision-making environment.
NOAH is a generative transformer that learns the full multimodal patient journey by integrating bidirectional time and a variational latent space. Trained on over 559 million clinical events from 431,000 hospital visits, it processes medical images, time‑series, numeric signals, categorical events, and both structured and unstructured records. The model supports autoregressive forecasting, zero‑shot classification, and counterfactual intervention simulation, yielding strong performance on clinical outcomes, ICD chapters, comorbidities, and time‑to‑event prediction.
arXiv:2607. 15432v1 Announce Type: cross Abstract: Emergency department (ED) boarding occurs when admitted patients remain in the ED while awaiting inpatient beds.
arXiv:2606. 05994v1 Announce Type: new Abstract: Medical knowledge graphs (MKGs) infused with clinical knowledge have been increasingly used to model electronic health records (EHRs) to support interpretable predictions in healthcare domain.
arXiv:2608. 14157v1 Announce Type: new Abstract: Mechanical ventilation is a critical life-support intervention, requiring dynamic adjustments to ventilator settings as a patient's condition evolves.
The paper introduces a Physics‑Informed Multi‑Agent Coordination framework that embeds calibrated BCMP queueing topologies into a decentralized multi‑agent reinforcement learning system for hospital patient flow. It formulates the problem as a Decentralized Partially Observable Markov Decision Process with coupled resource constraints, enabling departmental agents to negotiate patient routing and service scaling while exchanging localized action fingerprints to handle non‑stationarity. Empirical tests on MIMIC‑IV data show the approach reduces cumulative system delay compared to static Markovian models, heuristic dispatching, and independent multi‑agent baselines, all while preserving clinical safety constraints.