Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals

TL;DR

This paper introduces a Bayesian nonparametric approach to optimize personalized dental recall intervals, improving periodontal health outcomes by tailoring visit schedules based on patient data.

Contribution

It develops an interpretable, data-driven method combining Bayesian modeling and policy search to determine optimal recall intervals for periodontal disease management.

Findings

Improved periodontal health outcomes in simulations and real data.

No increase in average recall time compared to standard practices.

Method outperforms existing approaches in dental health optimization.

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 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.