Data Science and Applied Technology Core

数据科学与应用技术核心

• 批准号: 10631871
• 负责人: Todd Manini
• 金额: $ 8.97万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10631871
• 关键词: Address; Age; Aging; American; Applications Grants; Artificial Intelligence; Big Data; Biomedical Engineering; Biostatistics Core; COVID-19; Cell Aging; Cellular Phone; Clinical Data; Clinical Research; Clinical Trials; Clinical assessments; Collaborations; Common Data Element; Communication; Computer Models; Computer Systems; Computer software; Computers; Data; Data Analyses; Data Collection; Data Science; Data Sources; Development; Device Designs; Devices; Ecological momentary assessment; Ecosystem; Elasticity; Elderly; Electronic Health Record; Emerging Technologies; Engineering; Environment; Epidemiology; Etiology; Event; Expert Systems; Explosion; Florida; Future; Geriatrics; Gerontology; Geroscience; Goals; Harvest; Health; Health Technology; Intervention; Learning; Link; Location; Machine Learning; Medical; Mentors; Metabolism; Methodology; Modeling; Monitor; Output; Participant; Patients; Pattern; Perception; Phenotype; Physicians; Physiological; Population; Process; Research; Research Personnel; Resolution; Resources; Role; Science; Series; Services; Signal Transduction; Software Framework; Statistical Data Interpretation; Symptoms; Techniques; Technology; Testing; Time; Training; Translational Research; Universities; aging population; cohort; complex data; computer science; connected health; data ecosystem; data integration; data mining; data repository; data streams; deep learning; demographics; design; education research; flexibility; forgetting; handheld mobile device; improved; innovation; insight; interdisciplinary approach; mHealth; machine learning method; meetings; mobile computing; multimodal data; new technology; open source; phrases; predictive modeling; programs; research and development; sensor; smart watch; software development; tool; unstructured data; usability; wearable sensor technology

ABSTRACT Data Science and Applied Technology (DSAT) Core (RC4), the most recent addition to the University of Florida (UF) Older Americans Independence Center (OAIC), provides an interactive data and technology ecosystem for promote mobility and independence. Big data initiatives, applied technologies, and new methodological approaches for data science have exploded in many various environments, and the world is moving toward a connected system of computing and sensing components. The broadly used phrase “connected health” refers to an environment in which detailed data are collected on health, activity, location, and other aspects of the participating entities. Flexible control of the different interconnected and frequently communicating components can provide a rich set of applications that learn dynamically from data streams. Artificial intelligence (AI) promises to transform our ability to harness these data streams to advance science and improve health. The core uses techniques such as machine learning and deep learning to transform data utility through computer models that use data to learn, predict and – potentially – infer causation. Additionally, DSAT is on the forefront of mobile health (mHealth, smartphones and smartwatches) technologies that are changing the landscape for how patients and research participants communicate about their health in real time. Importantly, the core efforts are specifically targeted to older adults — a population that is often forgotten in these regards. DSAT provides a central hub of expertise in gerontology/geriatrics, computer engineering, mHealth, applied technology, and epidemiology. As a result, DSAT provides many unique attributes to the UF OAIC and nationally that: • Support OAIC cores, train and provide resources to REC Scholars, researchers and practitioners; • Advance interactive monitoring and remote health using mobile devices designed for older adults; • Use and develop data repositories and repurpose existing data to support new research on mobility; • Conduct machine learning, artificial intelligence, and pattern discovery analyses; • Collaborate with partnering OAIC cores to develop new research endeavors on promoting mobility; • Enhance externally supported projects. DSAT addresses many aspects of major national initiatives outlined in "Advancing Artificial Intelligence R&D" and "Emerging technologies to support an aging population" and is poised to meet these new initiatives. Given is interdisciplinary approach, it is well-suited to lead the UF OAIC into the future of connected health, mobile technology, advanced sensing, artificial intelligence and machine learning specifically geared toward promoting mobility and independence in older adults.

摘要 数据科学和应用技术（DSAT）核心（RC 4），最近加入佛罗里达大学 (UF)美国老年人独立中心（OAIC），提供交互式数据和技术生态系统 促进流动性和独立性。大数据计划、应用技术和新方法 数据科学的方法已经在许多不同的环境中爆炸，世界正在朝着一个 计算和传感组件的连接系统。广泛使用的短语“互联健康”是指 到一个环境中，收集有关健康，活动，位置和其他方面的详细数据， 参与实体。灵活控制不同的互连和频繁通信的组件 可以提供一组丰富的应用程序，从数据流中动态学习。人工智能（AI） 有望改变我们利用这些数据流来推进科学和改善健康的能力。的 核心使用机器学习和深度学习等技术，通过计算机将数据效用转化为 使用数据来学习、预测和潜在地推断因果关系的模型。此外，DSAT处于最前沿 移动的健康（mHealth、智能手机和智能手表）技术正在改变 患者和研究参与者如何在真实的时间内交流他们的健康状况。重要的是，核心 这些努力专门针对老年人-这一群体在这些方面往往被遗忘。DSAT 提供老年学/老年医学，计算机工程，移动健康，应用 技术和流行病学。因此，DSAT为UF OAIC提供了许多独特的属性， 在全国范围内： ·支持OAIC核心，为REC学者，研究人员和从业人员提供培训和资源; ·使用专为老年人设计的移动的设备推进交互式监测和远程健康; ·使用和开发数据存储库，并重新利用现有数据，以支持对移动性的新研究; ·进行机器学习、人工智能和模式发现分析; ·与合作伙伴OAIC核心合作，开发促进移动性的新研究工作; ·加强外部支持的项目。 DSAT解决了“推进人工智能研发”中概述的主要国家举措的许多方面 和“支持老龄化人口的新兴技术”，并准备满足这些新的倡议。给定 是跨学科的方法，它非常适合带领UF OAIC进入连接健康，移动的未来 技术，先进的传感，人工智能和机器学习，专门用于促进 老年人的行动能力和独立性。