CCSS: Discovery of Individualized Disease Features for Personalized Health Monitoring

帕金森氏症（PD）影响全球约600万人，预计到2040年将翻一番。药物和深部脑刺激等治疗干预措施需要逐步调整，从早期阶段的每天三次，到早期病例的每两小时一次。这些治疗调整是基于患者对药物的反应，这是从患者访谈中收集的。然而，患者访谈可能不可靠，并遭受回忆偏差，导致患者治疗过度或不足。因此，本项目提出开发方法和算法，与可穿戴传感器（电池供电的惯性测量单元）一起使用，以监测帕金森病患者在自然环境中对药物的反应。该提案是对精准医学国家努力的重大贡献。该项目的成功可能会带来全新的个体化治疗调整策略，从而为数百万PD患者的医疗服务和生活质量提供可观的改善。此外，一项综合教育和推广计划旨在促进佛罗里达大西洋大学（一所服务于西班牙裔的学院）以研究为基础的教育、传播和参与活动，以增加电气和计算机工程以及K-12学生中代表性不足的少数民族学生的管道。该项目开发了新的方法和算法，将可穿戴传感器数据转化为帕金森病患者对药物反应的临床可操作信息，从而在医院外实现个性化治疗调整。所提出的方法与目前的努力有很大的不同，因为它将开发一个集成的传感和计算框架，以探索用于识别患者特异性疾病特征的原始传感器数据。所提出的框架使用新颖的算法，结合可穿戴传感器，根据每个患者的疾病严重程度更新疾病特征。变革性设计解决了当前的技术障碍。首先，该项目开发了一个创新的传感和计算框架，以探索个性化疾病特征的多集融合原始传感器数据，从而显著提高对药物检测的反应准确性。这种新方法集成了张量分解来探索原始的多集传感器数据，用于数据驱动的疾病特征和强化学习，以得出何时更新个性化疾病特征的决策。其次，该项目将扩展开发的创新传感和计算框架，以支持使用电池供电的可穿戴传感器实现算法。新框架基于分布式设计（将计算成本高昂的算法组件卸载到服务器上）和轻量级架构（减少设备上的计算负载），并使可穿戴传感器设备仅在疾病特征更新时才将原始传感器数据传输到服务器。拟议研究的关键变革方面是数据分析工具的进步，用于PD患者的个性化监测，同时将研究转化为临床应用系统。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Parkinson's disease (PD) affects approximately six million people globally, which is predicted to double by 2040. Therapeutic interventions such as medication and Deep Brain Stimulation need to be progressively adjusted, from three times daily in early stages to as often as every two hours in advance cases. These therapeutic adjustments are based on the patients' response-to-medication, which is gathered from patient interviews. However, the patient interview can be unreliable and suffer from recall bias, resulting in over- or under-treatment of the patients. Therefore, this project proposes to develop methodologies and algorithms to be used along with wearable sensors (battery-powered inertial measurement units) to monitor the response-to-medication of Parkinson's disease patients in their natural environment. This proposal is a significant contribution to Precision Medicine national efforts. Success of this project may result in fundamentally new individualized therapy adjustment strategies, thereby providing considerable improvement in both healthcare delivery and quality of life for the millions of patients afflicted by PD. In addition, an integrated education and outreach program is designed to promote the research-informed education, dissemination, and engagement activities at the Florida Atlantic University, a Hispanic-serving Institute, to increase a pipeline of underrepresented minority students in electrical and computer engineering and K-12 students.This project develops novel methodologies and algorithms to translate the wearable sensors data into clinically actionable information about the response-to-medication of Parkinson's disease patients enabling personalized therapeutic adjustments outside hospital settings. The proposed approach is a major departure from the current efforts as it will develop an integrated sensing and computational framework to explore raw sensor data for identifying patient-specific disease features. The proposed framework uses novel algorithms, combined with wearable sensors, to update the disease features according to each patient's disease severity as the patient is being monitored. The transformative design addresses current technical obstacles. First, the project develops an innovative sensing and computation framework to explore the multiset fused raw sensor data for individualized disease features that could significantly improve response-to-medication detection accuracy. This new approach integrates tensor decomposition to explore raw, multiset sensor data for data-driven disease features and reinforcement learning to derive decisions on when to update the individualized disease features. Second, the project will extend the developed innovative sensing and computation framework to support the implementation of the algorithms with battery-powered wearable sensors. The new framework is based on a distributed design (to off-load computationally expensive components of the algorithms to a server) and a lightweight architecture (to reduce on-device computation load) and enables the wearable sensor device to transmit the raw sensors' data to a server only when the disease features are being updated. The key transformative aspect of the proposed research is the advancement of data analysis tools for personalized monitoring of PD patients while translating the research into a clinically applicable system.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.