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This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
This project will apply machine learning against a large data set to develop a model to both understand and predict surgical cancellations on individual pediatric patients at two pediatric surgical sites.
This project will enhance an existing Web-based portal, myADHDportal.com, to integrate behavioral tools alongside existing medication management tools for attention deficit hyperactivity disorder (ADHD).
Develops a patient Master Visit Registry (MVR), addressing the need for better information sharing among clinical organizations and enhancing their ability to give patients continuous high-quality care when they change providers. The MVR will expand upon an existing patient record-keeping system, while improving local handling and exchange of records.
Developed approaches to share data on patient clinical and diagnostic information across systems and created an implementation plan for systems integration.