Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals.

ABSTRACT: Tooth loss from periodontal disease is a major public health burden in the United States. Standard clinical practice is to recommend a dental visit every six months; however, this practice is not evidence-based, and poor dental outcomes and increasing dental insurance premiums indicate room for improvement. We consider a tailored approach that recommends recall time based on patient characteristics and medical history to minimize disease progression without increasing resource expenditures. We formalize this method as a dynamic treatment regime which comprises a sequence of decisions, one per stage of intervention, that follow a decision rule which maps current patient information to a recommendation for their next visit time. The dynamics of periodontal health, visit frequency, and patien

SUBMITTER: Guan Q 

PROVIDER: S-EPMC7531024 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature