CAREER: Data-Driven Personalized Chronic Disease Management

该学院早期职业发展计划（Career）奖将通过设计有效的数据驱动方法来实现慢性病的个性化管理，为促进国民健康和福利做出贡献。慢性疾病（如糖尿病和肥胖）的发病率和流行率的增加导致美国医疗保健支出的不可持续增长，并严重影响了福祉。目前，在相对均匀的患者群体的临床研究中，慢性病是通过根据患者的平均结果选择治疗方法来管理的。虽然众所周知，个体的疾病轨迹受到患者特异性特征的影响，但由于难以理清治疗计划、患者特征、疾病预后和治疗依从性之间的复杂关系，个性化治疗一直受到限制。该奖项将支持算法和模型的开发和验证，这些算法和模型结合了来自大型患者群体和特定患者的数据，以便将慢性病管理带入家庭环境。该奖项的教育部分将支持开发实践教学模块和研究指导机会，目的是扩大对运筹学及其对医疗保健问题的影响的兴趣，特别是在代表性不足的群体中。本研究将开发和分析新的工具，从医疗数据中识别因果关系，开发和分析新的工具，在考虑动态和随机变化的情况下，基于识别的因果关系进行个性化治疗，并通过基于糖尿病和减肥患者医疗数据集的模拟研究验证这些工具的有效性。该方法将开发新的统计和优化方法，以确定治疗方案、患者特征、疾病预后和坚持治疗之间的因果关系；然后利用这些估计的因果关系来个性化治疗。这项研究超越了现有的方法，这些方法只确定数据中的相关性，通常会导致次优治疗计划。本研究的预期结果包括新的统计和优化模型，新的理论分析，以及对已开发模型的优势和局限性的临床相关理解。该项目的成功有可能通过提高治疗的个性化和医疗保健服务的自动化来改善健康结果并降低医疗保健成本。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development Program (CAREER) award will contribute to the advancement of the national health and welfare by designing effective data-driven methods for personalized management of chronic diseases. Increased incidence and prevalence of chronic conditions such as diabetes and obesity have led to unsustainable growth in U.S. healthcare spending and have significantly impacted well-being. Currently, chronic diseases are managed by choosing treatments based on average patient outcomes in clinical studies with relatively homogeneous patient populations. While it is well-recognized that an individual's disease trajectory is influenced by patient-specific traits, personalized treatment has been limited by difficulties in disentangling the complex relationships between treatment plans, patient characteristics, disease prognosis, and adherence to treatments. This award will support the development and validation of algorithms and models that combine data from large patient cohorts and from specific patients in order to bring management of chronic diseases into the home setting. The educational components of this award will support development of hands-on teaching modules and research mentoring opportunities, with the aim of broadening interest in operations research and its impact on problems in healthcare, particularly among underrepresented groups. This research will develop and analyze new tools to identify causal relationships from medical data, develop and analyze new tools to personalize treatments based upon identified causal relationships while considering dynamic and stochastic variations, and validate effectiveness of these tools through simulation studies based on medical datasets of diabetes and weight loss patients. The approach will be to develop new statistical and optimization methods that identify causal relationships between treatment plans, patient characteristics, disease prognosis, and adherence to treatments; and then use these estimated causal relationships to personalize treatments. This research goes beyond existing approaches that identify only correlations in data and which generally lead to sub-optimal treatment plans. Anticipated outcomes of this research include new statistical and optimization models, new theoretical analyses, and a clinically-relevant understanding of the strengths and limitations of the developed models. Success in this project has the potential to improve health outcomes and reduce healthcare costs through increased personalization of treatment and automation of healthcare delivery.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.