Reimagining service delivery in the agentic era with Google Public Sector
cloud.google.com·21h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
Reconstructing and forecasting disease trajectories of patients with Alzheimer's disease using routine data in resource-constrained settings
✦ Why It Matters
Engineers and researchers can apply this framework to improve patient care in settings with limited resources.
Key Takeaways
- Utilize GNOVA for Alzheimer's disease trajectory reconstruction.
- Leverage routine data to forecast cognitive scores effectively.
- Focus on age, BMI, and APOE4 status as key predictors.
- Avoid reliance on costly neuroimaging for predictions.
How It Works
GNOVA integrates a GRU encoder to process irregular patient visit data and a Neural ODE decoder for continuous cognitive score estimation. This architecture allows for both interpolation and extrapolation of patient data, enabling clinicians to visualize complete disease trajectories.