Intervention-Aware Clinical World Model for Post-Op Outcome Forecasting in Cardiology
Many clinical prediction models treat post-intervention outcomes as a one-step mapping from baseline measurements to a future endpoint. However, recovery after a procedure often unfolds as an irregular trajectory: clinical observations, medication changes, repeat interventions, and physiological measurements are recorded asynchronously and can change risk assessment over time. We propose an intervention-aware clinical world model that represents each patient with a structured latent state and evolves it through time-ordered post-intervention events. The model first encodes baseline imaging int
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- PossiblePossibly related (embedding) · 47%Patient-Reported Outcomes, Machine Learning May Predict Breast Cancer Recurrence Earlier - Pharmacy Times →
- PossiblePossibly related (embedding) · 47%Artificial Intelligence in Cardiology: Applications in Diagnosis and Risk Prediction - Cureus →
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzySimilar title/name (fuzzy) · 59%amazon-science/chronos-forecasting →
“Fuzzy title match (0.73): “Intervention-Aware Clinical World Model for Post-Op Outcome ” ≈ “amazon-science/chronos-forecasting””
- LinkedLinked via arxiv author · 85%Yunsung Chung →
“Intervention-Aware Clinical World Model for Post-Op Outcome Forecasting in Cardiology”
- LinkedLinked via arxiv author · 85%Yingshuo Liu →
“Intervention-Aware Clinical World Model for Post-Op Outcome Forecasting in Cardiology”
- LinkedLinked via arxiv author · 85%Abboud F. Hassan →
“Intervention-Aware Clinical World Model for Post-Op Outcome Forecasting in Cardiology”
- LinkedLinked via arxiv author · 85%Han Feng →
“Intervention-Aware Clinical World Model for Post-Op Outcome Forecasting in Cardiology”