Forecasting and Control Methodology for Monitoring and Management of Chronic Diseases

慢性病监测和管理的预测和控制方法

• 批准号: 1161439
• 负责人: Mariel Lavieri
• 金额: $ 28万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1161439&HistoricalAwards=false
• 关键词: Forecasting; Control; Methodology; Monitoring; Management

The research objective of this award is to develop broadly applicable methods to improve monitoring and management of patients with chronic diseases. The systems engineering based modeling methodology will generate effective predictions of disease progression over time. Further, they will be integrated with real-time feedback-driven forecasting and control/optimization algorithms to help clinicians determine the interval of time until a particular chronic disease patient should be monitored next or an intervention should be considered by a physician. The research links population-based knowledge to patient-specific information measurements taken sequentially to determine the optimum monitoring intervals. Preliminary research indicates that linear Gaussian system models are effective for modeling progression, but existing theory will be extended to include controlled observations that optimize the tradeoff between intervals that are too short or too long. By using data to generate partially observable state space models, and by using higher dimensional state spaces, we bring a new perspective to this problem.The primary focus of this study is open-angle glaucoma, a major cause of blindness worldwide. If successful, the results of this research will indicate to clinicians when glaucoma progression has occurred and how chronic diseases such as glaucoma are likely to progress. In addition, our results will provide a recommendation on when to next monitor the patient. Such knowledge will improve the health outcomes of the population and also result in cost containment. The co-PIs will integrate these new methods into their courses and mentoring to impact graduate and undergraduate engineering students and medical students. Outcomes will be disseminated to engineering, public health, and medical communities through presentations and publications targeting highly visible journals in engineering and healthcare.

该奖项的研究目标是开发广泛适用的方法，以改善慢性病患者的监测和管理。基于系统工程的建模方法将对疾病随时间的进展进行有效预测。此外，它们将与实时反馈驱动的预测和控制/优化算法集成，以帮助临床医生确定特定慢性病患者接下来应该被监测或医生应该考虑干预的时间间隔。该研究将基于人群的知识与按顺序进行的患者特定信息测量联系起来，以确定最佳监测间隔。初步研究表明，线性高斯系统模型是有效的建模进展，但现有的理论将被扩展到包括控制的观察，优化之间的权衡间隔太短或太长。通过使用数据生成部分可观察的状态空间模型，并通过使用更高维的状态空间，我们带来了一个新的视角来解决这个问题。如果成功，这项研究的结果将向临床医生表明青光眼进展何时发生以及青光眼等慢性疾病可能如何进展。此外，我们的结果将为下次何时监测患者提供建议。这些知识将改善人口的健康状况，并控制成本。co-PI将把这些新方法整合到他们的课程和指导中，以影响研究生和本科工程学生和医学生。成果将通过针对工程和医疗保健领域知名期刊的演讲和出版物传播到工程、公共卫生和医学界。