Data-driven stochastic dynamic programming approaches for optimal planning of disease screening and chronic disorder management

数据驱动的随机动态规划方法，用于疾病筛查和慢性疾病管理的优化规划

• 批准号: RGPIN-2018-06596
• 负责人: Erenay, Fatih
• 金额: $ 4.52万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758141
• 关键词: Data; driven; stochastic; dynamic; programming

Given the transformation towards evidence-based and personalized decision making, powerful data-driven modeling approaches are needed to obtain personalized optimal intervention plans for practical engineering problems. We propose developing novel engineering tools based on stochastic dynamic programming and data analytics to address practical sequential decision making problems. Although proposed research will be applied to cancer screening and chronic-disease management, the focus is on developing engineering methodology.First, we propose a novel bicriteria partially observable Markov decision process (POMDP) to derive the optimal Pareto-efficient policies for particular screening problems. There is limited research on multicriteria POMDPs; the existing approaches are approximations based on methods including state discretization and machine learning. To solve our POMDP exactly, we propose a novel reformulation of the model as a constrained Markov decision process (MDP) by replacing the state space with a limited collection of historical screening observations. Under reasonable conditions, this reformulation provides a tractable model for which we aim to develop efficient solution procedures by reducing the state/action space via structural properties. Using clinical data from literature and Mayo Clinic, Rochester-MN, we will apply this formulation to determine the Pareto-efficient policies for colorectal cancer screening and surveillance. The proposed model may help develop insights to improve and personalize cancer screening practices, a significant contribution as cancer is the leading cause of death in Canada. The proposed approach will also improve the engineering knowledge on POMDPs for other engineering applications.Second, we propose a novel data-driven approach for modeling the progression of irreversibly deteriorating systems (e.g., chronic diseases) and their management. Many systems are monitored by complex scoring systems based on test/inspection results or scores from technical assessments. The proposed data analytics approach will process data from longitudinal records of inspections to identify critical events (tollgates) and estimate future progression of deterioration through these tollgates, by using methods including supervised machine learning, classification, and prediction models. The findings from these proposed prescriptive/predictive analytics tools will then be used to derive an MDP model to optimize the timing of palliative/assistive interventions to decrease the disutility due to deterioration. The proposed methodology will be applied to predict amyotrophic lateral sclerosis (ALS) progression, optimize the timing of ordering assistive devices, and maximize wellbeing of ALS patients using data from Mayo Clinic, Rochester.The proposed research will be extended to consider more general settings.

鉴于向基于证据和个性化决策做出的转变，需要强大的数据驱动建模方法来获得实用工程问题的个性化最佳干预计划。我们建议开发基于随机动态编程和数据分析的新型工程工具，以解决实际的顺序决策问题。尽管拟议的研究将应用于癌症筛查和慢性疾病管理，但重点是开发工程方法。首先，我们提出了一种新型的双粒子，部分可观察到的马尔可夫决策过程（POMDP）来得出针对特定筛查问题的最佳帕累托效率策略。关于多准则POMDP的研究有限。现有方法是基于包括州离散化和机器学习在内的方法的近似值。为了准确解决我们的POMDP，我们通过用有限的历史筛选观测值收集有限的收集来代替状态空间，从而提出了对模型的新颖重新重新制定，将模型作为约束的马尔可夫决策过程（MDP）。在合理的条件下，该重新制定提供了一个可聊天的模型，我们旨在通过通过结构性降低状态/行动空间来开发有效的解决方案程序。使用文献和梅奥诊所的临床数据，罗切斯特-MN，我们将应用此公式来确定用于结直肠癌筛查和监视的帕累托效率策略。拟议的模型可能有助于发展见解以改善和个性化癌症筛查实践，这是癌症是加拿大死亡的主要原因，这是加拿大的主要贡献。提出的方法还将改善其他工程应用程序的POMDP的工程知识。第二，我们提出了一种新型的数据驱动方法，用于建模不可逆转的恶化系统（例如慢性疾病）及其管理。许多系统都通过基于测试/检查结果或技术评估得分的复杂评分系统来监视。提出的数据分析方法将通过使用包括监督的机器学习，分类和预测模型在内的方法来处理检查检查的纵向记录，以识别关键事件（电话管理）并通过这些电话策略估算未来恶化的进展。然后，这些提出的规范/预测分析工具中的发现将用于得出MDP模型，以优化姑息/辅助干预措施的时机，以减少由于恶化而导致的分离。提出的方法将应用于预测肌萎缩性侧面硬化症（ALS）进展，优化订购辅助设备的时间，并使用罗切斯特梅奥诊所的数据最大化ALS患者的健康状况。拟议的研究将扩展，以考虑考虑更多的一般环境。