This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
This research applied machine learning to develop a model predicting surgical cancellations among pediatric patients, and found the feasibility in using these algorithms as a cost-effective quality-improvement measure.
This research explored the effectiveness of integrating behavioral tools into an evidence-based software to improve access to behavioral treatment strategies for children with attention deficit hyperactivity disorder.
This project developed and tested a tablet-based decision aid to assist primary care providers in applying patient-reported outcomes to smoking cessation and found that the tool facilitated more conversations about smoking cessation between patients and providers.
This project tested a pediatric voice therapy telehealth system and found that it was feasible to implement and well accepted by children and their families.
