SCH: INT: Collaborative Research: Development and analysis of new mathematical and statistical models for chronic pain

项目主任/首席调查员(最后、第一、中间)：艾布拉姆斯，丹尼尔，M 项目说明 1.智力方面的优点 (参见第节。2，第13-14页，“更广泛的影响”) 1.1导言和背景 1.1.1一般介绍 近几十年来，与此相关的数据的可用性有了惊人的增长。 广泛的微生物系统。这些数据使新的量化方法成为可能 到生物学，包括新的数学和统计模型的发展，这些模型带来了乐趣- 对生物系统工作原理的基本洞察。 另一个来源现在正在爆炸性地增长：生物医学数据。这些数据有很大的潜力 用于治疗人类疾病，但到目前为止，相对较少的数学模型 医学现象已经发展起来了。人们希望量化模型将允许 “个性化”或“精准”医学，其中治疗方案是基于 了解患者个体特征如何影响治疗效果。 对生物医学系统的深入数学理解也有望实现最优化 医疗干预：可将干预的实物和/或财务成本降至最低 给定的期望收益水平。 拟议研究的主要目标是开发新的综合数学模型 慢性疼痛患者主观疼痛的动态变化。这些模式将结合在一起 从新获得的患者数据中获得的具有洞察力的现有定性知识，目标是 在不久的将来将数据流整合到康宁公司。我们计划开发多种型号 同时使用多种方法，然后在协议的基础上选出最好的Rnodel(S 有客观的数据。 1.1.2生物应用背景：镰状细胞病 镰状细胞病(SCD)是一种慢性疾病，与频繁的内科并发症和 住院治疗。约90%的急性护理就诊是针对疼痛事件，30天重复- 市民化率高得惊人[27]。而影响这些高再利用率的因素有 缺乏了解、密切随访和持续使用止痛药已被证明会降低 提高再住院率。移动技术已成为医疗保健不可或缺的一部分 管理和Pl Shah最近开发的移动应用程序(智能应用程序-参见图1) 因为SCD协助记录疼痛和干预措施。 1.1.3关于混合方法的背景 也许是因为从业者的教育背景往往截然不同或典型的截然不同 应用程序、统计方法和机械方法在解决 只有一个问题。科学文献中的大多数尝试都出现在 神经网络[37，38，29]和化学工程[38，33，11]，它们在这些领域发挥着重要作用 计算而不是分析的角色。还进行了一些尝试，如医疗保健 应用：Rosenberg等人。[30]和亚当斯等人。[4]开发了一种模型，该模型结合了一个模型和一个模型。 临床系统采用统计模型来预测患者的CD4细胞计数和HIV 一项艾滋病毒研究中的病毒载量随时间的变化。蒂姆斯等人。[39]提出了一种动态系统方法 1 0MB编号0925-0001/0002(01/18版批准至2020年3月31日)页面_延续格式页面

Program Director/Principal Investigator (Last, First, Middle): Abrams, Daniel, M Project Description 1. Intellectual merit (see Sec. 2, pages 13-14, for "Broader impacts") 1.1 Introduction and background 1.1.1 General introduction During recent decades there has been an extraordinary growth in the availability of data relating to a wide range of microbiological systems. That data has enabled new quantitative approaches to biology, including the development of new mathematical and statistical models that given fun- damental insight into the workings of biological systems. Another source is now growing explosively: biomedical data. This data has significant potential for use in treatment of human disease, but thus far comparatively fewer mathematical models for medical phenomena have been developed. The hope is that quantitative models will allow for "personalized" or "precision" medicine, where treatment protocols are customized based on an understanding of how individual patient characteristics impact the effectiveness of the treatment. Deep mathematical understanding of biomedical systems also promises to allow for optimization of medical interventions: the physical and/or financial costs of intervention could be minimized for a given desired level of benefit. The broad goal of the proposed research is to develop new integrative mathematical models for the dynamics of subjective pain in patients suffering from chronic pain. These models will combine existing qualitative knowledge with insight gained from newly available patient data, with the goal of incorporating data streams corning on line in the near future. We plan to develop multiple models in parallel using a variety of approaches and then to select the best rnodel(s) based on agreement with objective data. 1.1.2 Background on biological application: Sickle cell disease Sickle cell disease (SCD) is a chronic illness associated with frequent medical complications and hospitalizations. Approximately 90% of acute care visits are for pain events, and 30-day reuti- lization rates are alarmingly high [27]. While factors influenci.ng these high re-utilization rates are poorly understood, close follow-up and continued use of pain medication has been shown to de- crease re-hospitalization rates. Mobile technology has become an integral part of health care management and Pl Shah's recently developed mobile application (SMART app - see Figure 1) for SCD assists with documentation of pain and interventions. 1.1.3 Background on hybrid approach Perhaps because of the often distinct educational backgrounds of practitioners or distinct typical applications, statistical and mechanistic approaches are not frequently combined in addressing a single problem. The majority of attempts in the scientific literature have appeared in the context of neural networks [37, 38, 29] and chemical engineering [38, 33, 11], where they largely play a computational rather than analytical role. Some attempts have also been made with medical applications: Rosenberg et al. [30] and Adams et al. [4] developed a model by combining a dy- namical systems approach with a statistical model to predict a patient's CD4 cell counts and HIV viral load over time in an HIV study. Timms et al. [39] proposed a dynamical systems approach 1 0MB No. 0925-0001/0002 (Rev. 01/18 Approved Through 03/31/2020) Page_ Continuation Format Page