arXiv AI
Learning a patient world model for health system simulation in intensive care
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 16
Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies
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 AI
Oct 15
PatientWM: Towards Generalizable Clinical World Models via Unified Patient States
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 AI
Jul 13
EHR-MPC: Inference-Time Control for Sepsis Treatment with Generative Patient Digital Twins
arXiv:2607. 08793v1 Announce Type: cross Abstract: Sepsis is a leading cause of mortality, yet optimal treatment policies remain contested.
arXiv AI
Oct 28
Learning response-aware patient dynamics for respiratory support
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 Machine Learning
Jun 4
VentAgent: When LLMs Learn to Breathe -- Multi-Objective Arbitration for ARDS Ventilation
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.
Hugging Face Trending Papers
Sep 8
NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting
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.