Bayesian Nonparametric Policy Search With Application to Periodontal Recall Intervals

• 成果类型: Article
• 署名作者: Guan, Qian; Reich, Brian J.; Laber, Eric B.; Bandyopadhyay, Dipankar
• 署名单位: North Carolina State University; Virginia Commonwealth University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1660169
• 发表日期: 2020
• 页码: 1066-1078
• 关键词: dynamic treatment regimes; learning-methods; RISK; PREVALENCE; disease; models

摘要

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 nonparametric 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. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.