Statistics-based Optimization Methods for Adaptive Interdisciplinary Pain Management

基于统计的自适应跨学科疼痛管理优化方法

• 批准号: 1434401
• 负责人: Jay Rosenberger
• 金额: $ 37.5万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1434401&HistoricalAwards=false
• 关键词: Statistics; based; Optimization; Methods; Adaptive

Chronic pain is a multidimensional problem that affects nearly 40 percent of the United States population and is estimated to incur $560-$635 billion in incremental healthcare costs and costs related to rehabilitation and lost productivity annually. With a growing number of treatment options and new medications, formulating an evidence-based, individually-tailored treatment plan has become increasingly complex. This research project develops new statistics-based optimization methods for adaptive interdisciplinary pain management that use patient data to recommend an interdisciplinary treatment regime for controlling pain outcomes. The research involves an interdisciplinary research team from the University of Texas (UT) at Arlington and uses data from the UT Southwestern Medical Center at Dallas. Because UT Southwestern uses a prevalent and standardized dataset, the research will be applicable to approximately 100 pain centers across the nation and will influence how interdisciplinary pain management is implemented. The research addresses four major topics in statistical optimization. One, the research studies how to coordinate the development of statistical meta-models and optimization algorithms. Specifically, several meta-models will be developed, which are accurate representations of the underlying system and can be globally optimized. The second major topic involves using a learning method to impute missing values in which the missing locations are in a block-wise structure. Three, the research develops an inverse-probability-of-treatment weighted estimators method for complex data. Finally, the research evaluates treatment solutions found from optimizing one prediction model with the other prediction models using simulation. Although this research is on pain management, the developed methodologies of this project can potentially be generalized to create complex adaptive treatment regimes for ailments other than pain.

慢性疼痛是一个多方面的问题，影响近40%的美国人口，估计每年增加5600 - 6350亿美元的医疗保健成本和与康复和生产力损失相关的成本。随着越来越多的治疗选择和新药，制定基于证据的个性化治疗计划变得越来越复杂。该研究项目开发了新的基于药物学的优化方法，用于适应性跨学科疼痛管理，使用患者数据推荐跨学科治疗方案来控制疼痛结局。这项研究涉及来自德克萨斯大学（UT）阿灵顿分校的跨学科研究小组，并使用来自达拉斯UT西南医学中心的数据。由于UT Southwestern使用普遍和标准化的数据集，该研究将适用于全国约100个疼痛中心，并将影响跨学科疼痛管理的实施方式。该研究解决了统计优化中的四个主要问题。一是研究统计元模型与优化算法的协调发展。具体而言，将开发几个元模型，这些元模型是底层系统的准确表示，并且可以进行全局优化。第二个主要主题涉及使用学习方法来估算缺失值，其中缺失位置处于块结构中。第三，研究了复杂数据的逆处理概率加权估计方法。最后，研究评估的治疗方案，从优化一个预测模型与其他预测模型使用模拟。虽然这项研究是关于疼痛管理的，但该项目开发的方法可能会被推广到为疼痛以外的疾病创建复杂的适应性治疗方案。