Student Investigator: Shengwei Zhang (WSE – Applied Math & Stats)
Mentor: Tinglong Dai (Carey), Kimia Ghobadi (WSE)

Operating rooms (ORs) are the most expensive and financially productive resource in a hospital, and any disruption in their workflow can have a detrimental effect on the rest of the hospital operations. One of the main challenges in designing an efficient OR schedule is the uncertainty in surgical case time duration. The goal of this project is to develop some (at least three) machine learning models that can effectively predict the surgical case duration and compare the predictive power of these models in a real clinical setting. The models will be developed by using a large retrospective data set in a 12-month period from Johns Hopkins Hospital, with 80% of the total cases used for training and other 20% for testing/validation. Related features are considered from three categories, including patient, personnel, and procedure.