The Advanced Analytics Research Core will support all four Research Projects at the University of Rochester Udall Center

高级分析研究核心将支持罗切斯特大学尤德尔中心的所有四个研究项目

• 批准号: 10459488
• 负责人: Michael P McDermott
• 金额: $ 30.14万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10459488
• 关键词: Algorithmic Analysis; Antiparkinson Agents; Biological Markers; Biometry; Caring; Cellular Phone; Clinic; Clinic Visits; Clinical; Clinical Trials; Collaborations; Communities; Computational Science; Computers; Data; Data Set; Development; Diagnosis; Disease Progression; Electrical Engineering; Enrollment; Evaluation; Faculty; Frequencies; Goals; Health Technology; Individual; Institution; LRRK2 gene; Measurement; Measures; Mentors; Observational Study; Outcome Measure; Pharmaceutical Preparations; Radio Waves; Research; Research Activity; Research Personnel; Research Project Grants; Research Support; Resources; Sample Size; Scientist; Severities; Standardization; Statistical Data Interpretation; Talents; Time; Universities; Visit; Work; advanced analytics; base; cohort; complex data; data management; data streams; digital; experience; improved; insight; interest; intervention effect; large datasets; machine learning algorithm; meetings; member; multidisciplinary; novel; novel therapeutics; organizational structure; predictive modeling; recruit; repository; response; sensor; signal processing; simulation; sound; synergism; tool; virtual; wearable sensor technology; webinar

SUMMARY The Advanced Analytics Research Core of the UR-Udall Center will unite deep biostatistical expertise and PD research experience at the University of Rochester with leading computational science talent from the University of Rochester, Johns Hopkins, Aston University, Sage Bionetworks, and MIT to support the UR-Udall Center's research projects and to conduct novel cross-project analyses. These projects will rely on large data sets, including the largest repository of PD clinical trial data (Project 1) and enormous data streams from smartphones (Project 3) and novel principally passive sensors (Project 4). The Advanced Analytics Research Core will rely on the talents of the Core's investigators, the resources of the individual institutions, and the data management expertise of the Center for Health + Technology to conduct its work. To enable cross-project analyses, we will enroll a group of “Super Users” (50 with PD and 20 without) that will undergo a standardized battery of in-clinic assessments over two years in addition to evaluation by at least three of the four sensors that are part of Research Projects 3 and 4, specifically smartphone, wearable sensor, video-based sensor, and radio wave-based sensor. This group will be one of the first to pair traditional clinical PD assessments with evaluations by multiple novel sensors in clinical and real-world settings. This highly characterized group will enable evaluation of the sensors' relative ability to differentiate those with PD from those without, to detect disease progression, to assess response to current medications, and to produce novel insights into PD that cannot be ascertained currently from in-clinic measurements. Beyond the work of the UR-Udall Center, the Advanced Analytics Research Core will be a resource for the Udall Center network and will also attract, develop, and mentor biostatistical and computational science talent for the greater PD community.

概括 Ur-udall中心的高级分析研究核心将团结深层的生物统计专业知识和PD 罗切斯特大学的研究经验，来自领先的计算科学人才 罗切斯特大学，约翰·霍普金斯大学，阿斯顿大学，Sage Bionetworks和MIT支持Ur-udall 中心的研究项目并进行新颖的跨项目分析。这些项目将依靠大数据 集合，包括最大的PD临床试验数据存储库（项目1）以及来自 智能手机（项目3）和新颖的主要被动传感器（项目4）。高级分析研究 核心将依靠核心调查人员的才华，各个机构的资源以及数据 卫生 +技术中心的管理专业知识。 为了启用跨项目分析，我们将注册一组“超级用户”（50个带有PD和20的无） 除了至少评估外，还要在两年内进行标准化的临界评估电池 四个是研究项目3和4的四个传感器中的三个，特别是智能手机，可穿戴传感器， 基于视频的传感器和基于无线电波的传感器。该小组将是第一个配对传统临床的人之一 PD评估通过临床和现实世界中多个新传感器进行评估。这很高 表征组将能够评估传感器的相对能力，使PD与PD区分开 那些没有检测疾病进展，评估对当前药物的反应并产生新颖的人 对PD的洞察力无法从链式内测量中确定。 除了Ur-udall中心的工作之外，高级分析研究核心还将成为 Udall Center网络，还将吸引，发展和心理生物统计学和计算科学人才 对于更大的PD社区。