SCH: EXP: Collaborative Research: Smart Asthma Management: Statistical modeling, prognostics, and intervention decision making

SCH：EXP：协作研究：智能哮喘管理：统计建模、预后和干预决策

• 批准号: 1343969
• 负责人: Shiyu Zhou
• 金额: $ 47.53万
• 依托单位: University of Wisconsin-Madison
• 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: 2014-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1343969&HistoricalAwards=false
• 关键词: SCH; EXP; Collaborative; Research; Smart

Asthma is a common lung disease with acute and chronic manifestations that impacts more than 22.2 million Americans or 7.9% of the population, including over 6.7 million children younger than 18 years of age. The cost of asthma is significant both for individuals and for the society as a whole. It is highly desirable to establish transformative technologies to improve the patient quality of life and reduce the cost of asthma management. The recent development in sensor and mobile computing technology provide great opportunities to establish Smart Asthma Management (SAM) systems and achieve a quantum leap in asthma management. Leveraging on the fast development of information infrastructure, patients can create a detailed temporal log recording their symptoms, medicine usage, and possibly vital physiological signals through an easy access to a website or their smart phones in SAM systems. This unprecedented continuous stream of patient-generated data in SAM systems provides us significant opportunities to better estimate patient condition and make clinical intervention decisions. However, since the information infrastructure of SAM has not become available until recently, very limited work is available for SAM systems. Against this background, this collaborative project aims to develop a suite of statistical modeling, monitoring, prognosis, and clinical intervention decision making methodologies based on a flexible yet rigorous multistate model to describe the evolving of patient conditions. The true underlying state of the patient is assumed unknown; however, there is reason to expect that it could be inferred from patient generated data such as the frequency of the rescue inhaler usage (the time and frequency of the rescue inhaler use is an important indicator of asthma control). Some anticipated advances include: (i) Multistate model with event intensity function as observations. The proposed methodology brings the mixed effect model and the multistate model into a unified framework to integrate the population information embedded in the historical records of multiple patients and the individual information collected in real-time in a quantitative way. (ii) Stochastic filtering approach for individual patient condition modeling and updating. The novel state space formulation enables efficient stochastic filtering algorithms to estimate and update the states and parameters in the multistate model. (iii) Clinical intervention decision support for patients and clinicians. The salient features of the proposed policy are that it is based on a condition-based policy and incorporates uncertainties in the patient condition model through a Partially Observable Markov Decision Process (POMDP) framework which has been widely used and proven to be very effective in the management of industrial systems. Plans are in place to evaluate the effectiveness of the resulting technologies in collaboration with clinical experts. The project is likely to contribute predictive technologies that could help reduce the cost and improve the quality of healthcare in the US, especially as it relates to effective management of chronic illnessess. Additional broader impacts of the project include enhanced research-based training opportunities for graduate and undergraduate students (including members of under-represented minorities) in healthcare engineering, statistics, and operation research; enrichment of the curricula in health systems in industrial engineering and operations research at the University of Wisconsin-Madison and the University of Iowa.

哮喘是一种常见的肺部疾病，具有急性和慢性表现，影响超过2220万美国人或人口的7.9％，其中包括670万年龄在18岁以下的儿童。哮喘的成本对于个人和整个社会都很重要。建立变革性技术以提高患者生活质量并降低哮喘管理成本是非常理想的。传感器和移动计算技术的最新发展提供了建立智能哮喘管理（SAM）系统并实现哮喘管理的量子飞跃的绝佳机会。利用信息基础设施的快速开发，患者可以通过轻松访问网站或SAM系统中的智能手机来创建详细的时间日志，记录其症状，药物使用情况以及可能的生理信号。 SAM系统中的这种空前的患者生成数据的连续流为我们提供了更好的机会，可以更好地估计患者状况并做出临床干预决策。但是，由于直到最近才能使用SAM的信息基础架构，因此SAM Systems的工作非常有限。在这种背景下，这个协作项目旨在开发一套统计建模，监测，预后和临床干预决策制定方法，以灵活而严格的多态模型来描述患者状况的发展。假定患者的真实潜在状态未知；但是，有理由期望可以从患者生成的数据（例如救援吸入器使用频率）（救援吸入器使用的时间和频率）中推断出来，这是哮喘控制的重要指标）。一些预期的进步包括：（i）具有事件强度函数作为观测值的多层模型。所提出的方法将混合效应模型和多层模型带入一个统一的框架，以整合嵌入在多名患者的历史记录中的人群信息和以定量方式实时收集的个人信息。 （ii）单个患者状况建模和更新的随机过滤方法。新颖的状态空间公式使有效的随机滤波算法能够估算和更新多层模型中的状态和参数。 （iii）对患者和临床医生的临床干预决策支持。拟议政策的显着特征是，它基于基于条件的政策，并通过部分可观察到的马尔可夫决策过程（POMDP）框架在患者状况模型中纳入了不确定性，该框架已被广泛使用并证明在工业系统的管理中非常有效。制定了计划，以评估与临床专家合作的最终技术的有效性。该项目可能有助于预测技术，以帮助降低成本并提高美国的医疗保健质量，尤其是与有效管理慢性病有关。该项目的其他更广泛的影响包括在医疗保健工程，统计和运营研究中为研究生和本科生（包括代表性不足的少数民族成员）增强基于研究的培训机会；威斯康星大学麦迪逊分校和爱荷华大学的工业工程和运营研究中卫生系统课程的丰富。