A Bilevel Optimization Approach to Integrating Personalized Treatment with Healthcare Resource Planning

将个性化治疗与医疗资源规划相结合的双层优化方法

• 批准号: 2321399
• 负责人: Tae Woo Lee
• 金额: $ 32.8万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321399&HistoricalAwards=false
• 关键词: Bilevel; Optimization; Approach; Integrating; Personalized

This award will contribute to the advancement of the national health and welfare by developing innovative methods to personalize the treatment of diabetic retinopathy (DR), the leading cause of blindness in American adults. Despite the increasing availability of emerging therapies that enable various effective DR treatment options, current clinical guidelines continue to rely on a one-size-fits-all approach. This award supports the development of data-driven methods that can lead to better utilization of emerging treatment techniques while addressing the unique clinical, systemic, and social needs of individual patients. With diabetic patients accounting for more than 10 percent of the US population, the successful establishment of personalized DR treatment guidelines will directly benefit numerous individuals. This project is expected to have a particular impact on the health and welfare of underrepresented patients by promoting equitable access to treatment. This award will fund a doctoral student in interdisciplinary research through multilateral collaboration and create educational materials for both operations research and healthcare professionals.This research aims to develop personalized DR treatment guidelines through a bilevel programming approach, which integrates two key perspectives on treatment design: (i) optimizing patient-specific treatment schedule over time through constrained, multi-agent Markov decision processes at the lower level, and (ii) determining eye clinic-level operational interventions to enhance resource allocation and provide personalized access to care at the upper level. The complex interaction between eye clinics and patients will be approximated by a large-scale mixed-integer program, which will be solved using decomposition methods and patient-specific heuristics calibrated with data from the electronic medical record system and a patient simulation model. Computational challenges in the lower-level problems will be addressed via dimensionality reduction techniques exploiting a unique solution structure and inverse optimization techniques identifying model parameters that facilitate easy-to-compute decision rules. This research will lead to advancements in the modeling and solution approaches for bilevel, multi-agent sequential decision-making.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项将通过开发创新方法来个性化治疗糖尿病视网膜病变（DR）（美国成年人失明的主要原因），为促进国家健康和福利做出贡献。尽管越来越多的新兴疗法可以实现各种有效的DR治疗选择，但目前的临床指南仍然依赖于一刀切的方法。该奖项支持数据驱动方法的开发，这些方法可以更好地利用新兴治疗技术，同时解决个体患者独特的临床，系统和社会需求。由于糖尿病患者占美国人口的10%以上，成功建立个性化DR治疗指南将直接使许多人受益。预计该项目将通过促进公平获得治疗，对代表性不足的病人的健康和福利产生特别影响。该奖项将通过多边合作资助一名跨学科研究的博士生，并为运筹学和医疗保健专业人员创建教育材料。该研究旨在通过双层编程方法开发个性化DR治疗指南，该方法整合了治疗设计的两个关键观点：（i）通过在较低级别的受约束的多代理马尔可夫决策过程随时间优化患者特异性治疗时间表，以及（ii）确定眼科诊所水平的操作干预，以增强资源分配并提供个性化的上层护理。眼科诊所和患者之间的复杂相互作用将通过一个大规模的混合整数规划来近似，该规划将使用分解方法和患者特定的算法来解决，该算法使用来自电子病历系统和患者模拟模型的数据进行校准。在较低层次的问题的计算挑战将通过降维技术，利用一个独特的解决方案结构和逆优化技术，识别模型参数，便于计算的决策规则来解决。这项研究将导致两层，多代理顺序决策的建模和解决方案的方法的进步。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。