SCH: EXP: Smart Adaptive Adherence-Enhancing Intervention Strategies for Breast Cancer Prevention

SCH：EXP：预防乳腺癌的智能适应性依从性增强干预策略

• 批准号: 1601084
• 负责人: Turgay Ayer
• 金额: $ 28.96万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1601084&HistoricalAwards=false
• 关键词: SCH; EXP; Smart; Adaptive; Adherence

Each year about 200,000 women are diagnosed with and more than 40,000 die from breast cancer, the most common female cancer in the US. Late detection significantly reduces survival; while 5-year survival is about 97 percent for early stage breast cancers, it is only about 20 percent for advanced stage cancers. Numerous clinical trials and community setting analyses have shown that repeat mammography use can significantly reduce breast cancer mortality. The reduction in breast cancer mortality due to screening however, is contingent upon adhering to screening recommendations and having consecutive on-schedule mammograms. Therefore, women who do not adhere to receiving repeat mammograms are at risk for developing advanced stage or incurable breast cancers. Indeed, adherence to cancer screening has been identified as a national top priority to reduce cancer mortality. In line with this initiative, the research objective of this project is to optimize the design and allocation of adaptive adherence-enhancing intervention (AEI) strategies to improve overall adherence to mammography screening, while reducing unnecessary costs. From a societal perspective, this research has the potential to significantly improve the efficiency of adherence-enhancing intervention strategies for more effective breast cancer prevention. Results from this research can inform breast cancer prevention policies at the level of the individual health plan, a state's comprehensive cancer control plan, and also at the national level in terms of guideline development. This project will also have an immediate impact on integration of research and learning, and enhancing diversity. Under this project, a PhD student will be trained to apply systems modeling methodologies to healthcare area. In addition, the investigators will engage several minority students into these research activities, and aim to attract them to engineering with a focus on healthcare. This research will apply machine learning and adaptive stochastic dynamic control methodologies to learn patients' responses to adherence-enhancing interventions and optimize the use of intervention strategies accordingly. If successful, this project will make several intellectual contributions. First, this will be the first study to optimize the design and allocation of adaptive AEI strategies for sustained mammography use. The team will develop flexible adaptive stochastic control models that capture key disease and intervention dynamics, conduct in depth structural analysis of analytical models, and develop tailored solution algorithms. In parameterizing such models, the team will use large national datasets to inform the models. Further, the team will test policies derived by the analytical models against some actual policies through a detailed simulation model to evaluate possible solutions and estimate the impact. The project's approaches are general and could be applied to other chronic diseases with historically low adherence rates to screening.

每年约有20万名妇女被诊断出患有乳腺癌，这是美国最常见的女性癌症。晚期检测可显着降低生存；尽管早期乳腺癌的5年生存率约为97％，但晚期阶段癌症只有20％。许多临床试验和社区设置分析表明，重复使用乳房X线摄影可以显着降低乳腺癌的死亡率。然而，由于筛查而引起的乳腺癌死亡率的降低是遵循筛查建议并连续进行乳房X线照片的筛查。因此，不遵守重复乳房X线照片的妇女有患高级阶段或无法治愈的乳腺癌的风险。实际上，遵守癌症筛查已被确定为降低癌症死亡率的全国重中之重。与该计划相一致，该项目的研究目标是优化适应性依从性增强干预措施（AEI）策略的设计和分配，以提高整体遵守乳房X线摄影筛查，同时降低不必要的成本。从社会的角度来看，这项研究有可能显着提高依从性增强干预策略的效率，以预防更有效的乳腺癌。这项研究的结果可以在个人健康计划，州的全面控制计划以及在指导方向发展方面的个人健康计划，国家全面的癌症控制计划以及国家水平上的乳腺癌预防政策。该项目还将立即对研究和学习的整合以及增强多样性产生影响。在该项目下，将对博士生进行培训，以将系统建模方法应用于医疗保健领域。此外，调查人员将吸引几个少数族裔学生参加这些研究活动，并旨在吸引他们进入工程学，重点关注医疗保健。这项研究将应用机器学习和自适应随机动态控制方法来学习患者对依从性增强干预措施的反应，并相应地优化干预策略的使用。 如果成功，该项目将做出一些智力贡献。首先，这将是首次优化自适应AEI策略的设计和分配以持续乳房X线摄影的研究。该团队将开发灵活的自适应随机控制模型，以捕获关键的疾病和干预动态，对分析模型进行深度结构分析，并开发量身定制的溶液算法。在参数化此类模型时，团队将使用大型国家数据集通知模型。此外，该团队将通过分析模型来测试通过详细的仿真模型对某些实际策略得出的策略，以评估可能的解决方案并估算影响。该项目的方法是一般的，可以应用于其他历史较低依从性筛查的慢性疾病。