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

SCH：INT：合作研究：慢性疼痛新数学和统计模型的开发和分析

基本信息

批准号：
10180356
负责人：
Daniel M Abrams
金额：
$ 36.85万
依托单位：
NORTHWESTERN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-13 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10180356
关键词：
Address Agreement Analgesics Biological Biology CD4 Lymphocyte Count Car Phone Characteristics Chemical Engineering Chronic Disease Classification Custom Data Data Science Development Differential Equation Documentation Educational Background Event Financial cost Future Goals Growth HIV Hospitalization Hybrids Intervention Knowledge Literature Machine Learning Mathematics Medical Medical Device Methods Microbiology Mining Modeling Names Optimum Populations Pain Patients Pharmaceutical Preparations Play Principal Investigator Process Reporting Research Role Science Sickle Cell Anemia Source Statistical Data Interpretation Statistical Models Stream System Time Treatment Effectiveness Treatment Protocols Viral Load result Visit acute care base biological systems biomedical data science chronic pain comparative data streams dynamic system follow-up health management human disease individual patient insight intervention cost mathematical model mathematical sciences medical complication mobile application mobile computing neural network patient population precision medicine predictive modeling programs readmission rates research and development statistics theories tool

项目摘要

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

计划总监/首席调查员（最后，第一，中间）：艾布拉姆斯，丹尼尔，M 项目描述 1。智力优点（有关“更广泛的影响”，请参见第2节，第13-14页） 1.1简介和背景 1.1.1一般简介在近几十年中，与数据有关到广泛的微生物系统。该数据已实现了新的定量方法生物学，包括开发新的数学和统计模型，这些模型具有有趣的对生物系统运作的大型洞察力。现在的另一个来源正在爆炸性地增长：生物医学数据。这些数据具有巨大的潜力用于治疗人类疾病，但迄今为止的数学模型相对较少已经开发了医疗现象。希望定量模型将允许 “个性化”或“精度”药物，根据一个基于治疗方案了解个体患者特征如何影响治疗的有效性。对生物医学系统的深刻数学理解也有望进行优化医疗干预措施：可以将干预的物理和/或经济成本最小化给定所需的利益水平。拟议的研究的广泛目标是开发新的综合数学模型患有慢性疼痛的患者主观疼痛的动态。这些型号将结合在一起现有的定性知识，从新近获得的患者数据中获得的洞察力，目标在不久的将来将数据流纳入康宁。我们计划开发多种模型并行使用多种方法，然后根据一致选择最佳的rnodel 使用客观数据。 1.1.2生物应用背景：镰状细胞疾病镰状细胞病（SCD）是一种慢性疾病，与频繁的医疗并发症和住院。大约90％的急性护理就诊是用于疼痛事件，而30天的Reuti- 液化率令人震惊[27]。虽然因素影响这些高的重新利用率是知识渊博的是，近访和继续使用止痛药已证明折痕重新院长率。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 - 请参见图1） SCD有助于记录疼痛和干预措施。 1.1.3混合方法背景也许是因为从业人员的教育背景通常很独特或典型的典型应用程序，统计和机械方法在解决一个方面并不经常合并单个问题。科学文献中的大多数尝试都出现在上下文中神经网络[37，38，29]和化学工程[38，33，11] 计算角色而不是分析角色。医疗也进行了一些尝试应用：Rosenberg等。 [30]和Adams等。 [4]通过结合dy-开发了一个模型具有统计模型的名义系统方法，以预测患者的CD4细胞计数和HIV 在HIV研究中，病毒载荷随着时间的流逝。 Timms等。 [39]提出了动态系统方法 1 0MB编号0925-0001/0002（修订版01/18通过03/31/2020批准）Page_连续格式页面