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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：
10231168
负责人：
Daniel M Abrams
金额：
$ 27.05万
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 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10231168
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

项目负责人/主要研究者（最后，第一，中间）：Abrams，丹尼尔，M 项目描述 1.智力上的优点 (see秒第2段，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，与下列方面有关的数据的可用性有了惊人的增长：广泛的微生物系统。这些数据使新的定量方法成为可能生物学，包括新的数学和统计模型的发展，对生物系统运作的重要洞察力另一个数据源正在爆炸性增长：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，医学现象已经发展起来。希望是定量模型将允许 “个性化”或“精确”医学，其中治疗方案是根据患者的需求定制的。了解个体患者特征如何影响治疗效果。对生物医学系统的深刻数学理解也有望允许优化医疗干预：干预的物理和/或经济成本可以最大限度地减少，一个给定的期望的利益水平。拟议研究的广泛目标是开发新的综合数学模型，慢性疼痛患者的主观疼痛动力学。这些型号将联合收割机现有的定性知识以及从新获得的患者数据中获得的见解，目标是在不久的将来，将康宁的数据流整合到网上。我们计划开发多种型号并行使用各种方法，然后根据一致性选择最佳模型客观的数据。 1.1.2生物学应用背景：镰状细胞病镰状细胞病（SCD）是一种与频繁的医疗并发症相关的慢性疾病，住院治疗大约90%的急性护理访视是为了疼痛事件，30天的reuti- 这是一个令人震惊的高度[27]。influenci.ng这些高重复利用率的因素是了解不多，密切随访和继续使用止痛药已被证明是去- 提高再住院率。移动的技术已成为医疗保健不可或缺的一部分管理和Pl Shah最近开发的移动的应用程序（SMART应用程序-见图1）用于SCD，协助记录疼痛和干预措施。 1.1.3混合方法的背景也许是因为从业者往往不同的教育背景或不同的典型应用程序、统计和机械方法在解决问题时并不经常结合起来。单一问题。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]，它们在很大程度上发挥着一个计算而不是分析的角色。一些尝试也已经与医疗应用：Rosenberg等人。[30]和亚当斯等人。[4]开发了一个模型，用统计模型预测患者的CD 4细胞计数和HIV 病毒载量随时间的变化。Timms等人[39]提出了一种动力系统方法 1 0 MB编号0925-0001/0002（修订版01/18批准至03/31/2020）页_续格式页

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure.

利用自然学习处理来揭示因心力衰竭入院的患者临床记录中的主题。

DOI：
10.1109/embc48229.2022.9871400
发表时间：
2022
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子：
0
作者：
Agarwal,Ankita;Thirunarayan,Krishnaprasad;Romine,WilliamL;Alambo,Amanuel;Cajita,Mia;Banerjee,Tanvi
通讯作者：
Banerjee,Tanvi

Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study.

DOI：
10.2196/36998
发表时间：
2022-06-23
期刊：
JMIR FORMATIVE RESEARCH
影响因子：
2.2
作者：
Padhee, Swati;Nave, Gary K., Jr.;Banerjee, Tanvi;Abrams, Daniel M.;Shah, Nirmish
通讯作者：
Shah, Nirmish

Improving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimization.

使用多目标优化提高抽象临床文本摘要的事实准确性。