SCH: EXP: Collaborative Research: Smart Asthma Management: Statistical Modeling, Prognostics, and Intervention Decision Making

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7.9%的人口，包括超过670万18岁以下的儿童。哮喘的成本对于个人和整个社会都是巨大的。建立变革性技术以改善患者生活质量并降低哮喘管理成本是非常可取的。传感器和移动的计算技术的最新发展为建立智能哮喘管理（SAM）系统并实现哮喘管理的飞跃提供了绝佳的机会。利用信息基础设施的快速发展，患者可以通过SAM系统轻松访问网站或智能手机，创建详细的时间日志，记录他们的症状，药物使用情况以及可能的重要生理信号。SAM系统中前所未有的患者生成数据的连续流为我们提供了更好地估计患者状况和做出临床干预决策的重要机会。然而，由于SAM的信息基础设施直到最近才可用，SAM系统的工作非常有限。在此背景下，这个合作项目的目的是开发一套统计建模，监测，预后和临床干预决策方法的基础上，灵活而严格的多状态模型来描述患者病情的演变。假设患者的真实潜在状态未知;然而，有理由预期可以从患者生成的数据（例如急救吸入器使用频率）推断出患者的真实潜在状态（急救吸入器使用的时间和频率是哮喘控制的重要指标）。一些预期的进展包括：（i）以事件强度函数作为观测值的多状态模型。所提出的方法将混合效应模型和多状态模型纳入一个统一的框架，以定量的方式整合嵌入在多个患者的历史记录中的群体信息和实时收集的个体信息。(ii)用于个体患者状况建模和更新的随机过滤方法。新的状态空间制定有效的随机滤波算法，以估计和更新的多状态模型中的状态和参数。(iii)为患者和临床医生提供临床干预决策支持。建议的政策的显着特点是，它是基于一个条件为基础的政策，并通过部分可观察马尔可夫决策过程（POMDP）框架，已被广泛使用，并证明是非常有效的工业系统的管理中的患者病情模型中的不确定性。我们已制定计划，与临床专家合作评估所产生技术的有效性。该项目可能有助于预测技术，有助于降低成本和提高美国医疗保健的质量，特别是因为它与慢性病的有效管理有关。该项目的其他更广泛的影响包括增强研究生和本科生（包括代表性不足的少数民族成员）在医疗保健工程，统计和运筹学的研究为基础的培训机会;在工业工程和运筹学在威斯康星大学麦迪逊分校和爱荷华州的大学在卫生系统的课程丰富。

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.