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

该奖项将通过开发个性化治疗糖尿病视网膜病变(DR)的创新方法来促进国家健康和福利的进步，DR是美国成年人失明的主要原因。尽管越来越多的新兴疗法能够实现各种有效的DR治疗选择，但当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的治疗技术，同时满足个别患者独特的临床、系统和社会需求。由于糖尿病患者占美国人口的10%以上，成功建立个性化的DR治疗指南将直接使无数人受益。预计该项目将通过促进公平获得治疗，对任职人数不足的患者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。这项研究旨在通过双层规划方法开发个性化的DR治疗指南，该方法整合了治疗设计的两个关键观点：(I)通过较低级别的受限、多智能体马尔可夫决策过程，随着时间的推移优化针对患者的治疗计划，以及(Ii)确定眼科诊所级别的操作干预措施，以增强资源分配并在较高级别提供个性化的护理机会。眼科诊所和患者之间的复杂互动将由一个大规模的混合整数程序来近似，该程序将使用分解方法和特定于患者的启发式方法来解决，这些启发式方法使用来自电子病历系统的数据和患者模拟模型进行校准。将通过利用独特的解结构的降维技术和识别便于计算的决策规则的模型参数的反向优化技术来解决较低级别问题中的计算挑战。这项研究将导致两层、多主体顺序决策的建模和解决方法的进步。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.