Machine learning ensemble models predict total charges and drivers of cost for transsphenoidal surgery for pituitary tumor.

Objective: Efficient allocation of resources in the healthcare system enables providers to care for more and needier patients. Identifying drivers of total charges for transsphenoidal surgery (TSS) for pituitary tumors, which are poorly understood, represents an opportunity for neurosurgeons to reduce waste and provide higher-quality care for their patients. In this study the authors used a large, national database to build machine learning (ML) ensembles that directly predict total charges in this patient population.