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

哮喘是一种常见的急性和慢性肺部疾病，影响了2220多万美国人，占人口的7.9%，其中包括670多万18岁以下的儿童。无论是对个人还是对整个社会来说，哮喘的代价都是巨大的。建立变革性的技术来改善患者的生活质量和降低哮喘管理的成本是非常可取的。传感器和移动计算技术的最新发展为建立智能哮喘管理（SAM）系统和实现哮喘管理的量子飞跃提供了巨大的机会。借助信息基础设施的快速发展，患者可以通过访问SAM系统中的网站或智能手机，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。在SAM系统中，这种前所未有的患者生成的连续数据流为我们提供了更好地评估患者病情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，因此SAM系统的可用工作非常有限。在此背景下，该合作项目旨在开发一套基于灵活而严谨的多状态模型的统计建模、监测、预后和临床干预决策方法，以描述患者病情的演变。假定患者的真实潜在状态是未知的；然而，有理由期望可以从患者产生的数据中推断，例如使用救援吸入器的频率（使用救援吸入器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：(1)以事件强度函数为观测值的多状态模型。该方法将混合效应模型和多状态模型整合到一个统一的框架中，将多个患者历史记录中嵌入的群体信息与实时采集的个体信息进行定量整合。（ii）随机滤波方法用于个体患者病情建模和更新。这种新的状态空间公式使得随机滤波算法能够有效地估计和更新多状态模型中的状态和参数。（三）为患者和临床医生提供临床干预决策支持。所提出的策略的显著特征是，它基于基于条件的策略，并通过部分可观察马尔可夫决策过程（POMDP）框架将不确定性纳入患者病情模型，该框架已被广泛使用并被证明在工业系统管理中非常有效。已制定计划，与临床专家合作评估所产生技术的有效性。该项目可能会贡献预测技术，有助于降低成本，提高美国医疗保健的质量，特别是在慢性病的有效管理方面。该项目的其他更广泛的影响包括，在医疗保健工程、统计学和运筹学方面，为研究生和本科生（包括代表性不足的少数族裔）提供更多基于研究的培训机会；丰富了威斯康星大学麦迪逊分校和爱荷华大学工业工程和运筹学卫生系统课程。

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.