Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals.

TitleBayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals.
Publication TypeJournal Article
Year of Publication2020
AuthorsGuan, Qian, Brian J. Reich, Eric B. Laber, and Dipankar Bandyopadhyay
JournalJ Am Stat Assoc
Date Published2020

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 patient compliance are complex, yet the estimated optimal regime must be interpretable to domain experts if it is to be integrated into clinical practice. We combine non-parametric Bayesian dynamics modeling with policy-search algorithms to estimate the optimal dynamic treatment regime within an interpretable class of regimes. Both simulation experiments and application to a rich database of electronic dental records from the HealthPartners HMO shows that our proposed method leads to better dental health without increasing the average recommended recall time relative to competing methods.

Alternate JournalJ Am Stat Assoc
Original PublicationBayesian nonparametric policy search with application to periodontal recall intervals.
PubMed ID33012901
PubMed Central IDPMC7531024
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 DE024984 / DE / NIDCR NIH HHS / United States