PatientWM: Towards Generalizable Clinical World Models via Unified Patient States
To tackle these limitations, we propose PatientWM, a clinical world-model framework built around a unified patient state and sequential latent history.
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Key points
- Clinical decisions depend on anticipating how a patient's condition will evolve as treatment is adjusted and new evidence becomes available.
- Existing medical world models capture patient-state evolution, but often define the evolving state around a particular modality or task, limiting reuse as inputs or prediction targets change.
- Availability-aware encoders integrate observations available at each decision time into the patient state, while typed decoders produce task-specific clinical readouts without defining the state around any single target.
- PatientWM further conditions each next-state prediction on the current state and supplied action together with a bounded, ordered history of preceding latent patient states and clinical actions, preserving recent treatment and response context.
Sources (1)
- [1]PatientWM: Towards Generalizable Clinical World Models via Unified Patient StatesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 04:26 PM
To tackle these limitations, we propose PatientWM, a clinical world-model framework built around a unified patient state and sequential latent history.
Clinical decisions depend on anticipating how a patient's condition will evolve as treatment is adjusted and new evidence becomes available.
Extractive summary: sentences quoted from the sources.
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