Clinical prediction improves when models treat recovery as an evolving process rather than a static snapshot—incorporating asynchronous post-procedure events and imaging can significantly boost outcome forecasting accuracy for cardiac interventions.
This paper presents a clinical AI model that predicts post-surgery outcomes in heart rhythm procedures by tracking how a patient's condition evolves over time.