RII Track 2 FEC: Multi-Scale Integrative Approach to Digital Health: Collaborative Research and Education in Smart Health in West Virginia and Arkansas

RII Track 2 FEC：数字健康的多尺度综合方法：西弗吉尼亚州和阿肯色州智能健康的合作研究和教育

• 批准号: 1920920
• 负责人: Donald Adjeroh
• 金额: $ 400万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1920920&HistoricalAwards=false
• 关键词: RII; Track; FEC; Multi; Scale

One potential approach to improve overall health outcomes and reduce healthcare costs is through the use of artificial intelligence techniques that can exploit the enormous amount of information embedded in huge and diverse health-related datasets. To leverage this large amount and large variety of information the project will develop new methods to address core research questions in artificial intelligence. Specifically, this project will develop and disseminate computational methods to maintain privacy while analyzing large datasets, develop and disseminate measures and methods to increase the transparency of data analysis and thus increase trust in the analysis results, and develop and disseminate methods to measure and reduce bias in big data sets. While privacy, transparency and bias reduction are important aspects of artificial intelligence in general, addressing these topics is especially urgent for health-related data and applications. The long-term goal is to accelerate decision making for smart health applications, through the development and application of advanced artificial intelligence techniques that can take advantage of available massive heterogeneous health-related datasets in an unbiased way. Successful realization of this goal will have significant broader impacts by spurring economic activity through improved workforce development in key technology areas of data science, artificial intelligence, and smart health.This project proposes a collaboration involving five partner institutions in West Virginia and Arkansas, and seven target primarily undergraduate institutions across the two states. Innovation in the project stems from the proposed techniques addressing difficult research challenges in artificial intelligence and data analytics, such as privacy-preserving data analytics, novel explanation-centric artificial intelligence techniques, multiscale approaches to exploiting diverse and massive health datasets using heterogeneous information network embedding, and implementation of new multi-view patient profile algorithms. Further innovation comes from the proposed non-trivial adaptations of these techniques for rapid and accurate decision making in smart health, by using large-scale computational deep learning techniques. High school students will be involved in STEM-related activities, while undergraduate and graduate students will be trained on leading-edge artificial intelligence and big data techniques and how these can be adapted for smart health applications. Workshops and summer schools will be used to educate students and faculty on research topics being studied in the collaboration, and to provide practical hands-on training on popular artificial intelligence platforms.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.

改善整体健康结果和降低医疗成本的一种潜在方法是使用人工智能技术，该技术可以利用嵌入在庞大而多样化的健康相关数据集中的大量信息。为了利用这些大量和各种各样的信息，该项目将开发新的方法来解决人工智能的核心研究问题。 具体而言，该项目将开发和传播在分析大型数据集时维护隐私的计算方法，开发和传播提高数据分析透明度的措施和方法，从而提高对分析结果的信任，并开发和传播测量和减少大数据集偏见的方法。 虽然隐私、透明度和减少偏见是人工智能的重要方面，但解决这些问题对于与健康相关的数据和应用来说尤为紧迫。长期目标是通过开发和应用先进的人工智能技术，以公正的方式利用可用的大量异构健康相关数据集，加速智能健康应用的决策。这一目标的成功实现将通过改善数据科学、人工智能和智能健康等关键技术领域的劳动力发展来刺激经济活动，从而产生更广泛的影响。该项目提议与西弗吉尼亚州和阿肯色州的五所合作机构以及两个州的七所主要针对本科院校的合作。该项目的创新源于所提出的技术，这些技术解决了人工智能和数据分析中的困难研究挑战，例如隐私保护数据分析，以信息为中心的新型人工智能技术，使用异构信息网络嵌入利用多样化和大规模健康数据集的多尺度方法，以及新的多视图患者配置文件算法的实施。进一步的创新来自于通过使用大规模计算深度学习技术，对这些技术进行非平凡的调整，以实现智能健康中快速准确的决策。高中生将参与STEM相关活动，而本科生和研究生将接受前沿人工智能和大数据技术的培训，以及如何将这些技术应用于智能健康应用。通过举办研讨会和暑期学校，为学生和教师提供关于合作研究课题的教育，并提供关于流行人工智能平台的实践培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Learning Representations for Masked Facial Recovery

• DOI: 10.1007/978-3-031-20713-6_2
• 发表时间: 2022-12
• 期刊: ArXiv
• 作者: Zaigham A. Randhawa;Shivang Patel;D. Adjeroh;Gianfranco Doretto

Deep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction

• DOI: 10.1016/j.jcmg.2021.04.010
• 发表时间: 2021-10-04
• 期刊: JACC-CARDIOVASCULAR IMAGING
• 影响因子: 14
• 作者: Pandey, Ambarish;Kagiyama, Nobuyuki;Sengupta, Partho P.
• 通讯作者: Sengupta, Partho P.

Using Computational Social Science Techniques to Identify Coordinated Cyber Threats to Smart City Networks

使用计算社会科学技术识别智能城市网络的协调网络威胁

• 发表时间: 2021
• 期刊: Sustainable civil infrastructures
• 作者: Al-Assad, M;Spann, B;Al-khateeb, S;Agarwal, N
• 通讯作者: Agarwal, N

Blog Data Analytics Using Blogtrackers.

使用 Blogtrackers 进行博客数据分析。

• DOI: 10.1007/978-3-030-67044-3_6
• 发表时间: 2021
• 期刊: Big Data and Social Media Analytics.
• 作者: Obadimu, A.;Hussain, M.N.;Agarwal, Nitin
• 通讯作者: Agarwal, Nitin

Achieving Differential Privacy in Vertically Partitioned Multiparty Learning

在垂直分区多方学习中实现差异隐私

• DOI: 10.1109/bigdata52589.2021.9671502
• 发表时间: 2021
• 期刊: 2021 IEEE International Conference on Big Data (Big Data
• 作者: Xu, Depeng;Yuan, Shuhan;Wu, Xintao
• 通讯作者: Wu, Xintao