Spokes: MEDIUM: MIDWEST: Collaborative: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest

辐条：媒介：中西部：协作：社区驱动的数据工程，用于中西部农村地区的药物滥用预防

• 批准号: 1956009
• 负责人: Amit Sheth
• 金额: $ 12万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1956009&HistoricalAwards=false
• 关键词: Spokes; MEDIUM; MIDWEST; Collaborative; Community

The opioid crisis ravaging Ohio and the Midwest disproportionally affects small and rural communities. Harnessing and deploying data holds promise for developing a response to this crisis by policymakers, healthcare providers, and citizens of the communities. Currently, there are many barriers to getting data into the hands of individuals on the frontlines. Crucial data are siloed across law enforcement, public health departments, hospitals and clinics, and county administrations; data often are inaccurate or collected in non-standard ways across different agencies and departments; the stigma of drug abuse limits accurate reporting of drug-related deaths; and information is not shared with the community and other stakeholders because of the lack of a privacy and security framework. Such barriers, for example, prevent individuals with addictions or their families and friends from locating available treatment centers or obtaining other important information in a timely way. Similarly, it is difficult for first responders and healthcare providers to obtain critical up-to-date information. In predominantly rural counties, these challenges are especially daunting because there is often poor connectivity and communication infrastructure. This Big Data Spoke project involves developing scalable, flexible, and connectivity-rich data-driven approaches to address the opioid epidemic. The cyberinfrastructure framework, OpenOD, will be initially designed and deployed in small and rural communities in Appalachia Ohio and the Midwest, where the need for data and connection are greatest. Based upon significant community input, OpenOD will also create end-user applications or enterprise solutions to support stakeholders and communities to mount a response they feel will be most efficient and beneficial at the local level. As a Spoke to NSF?s Midwest Big Data Hub, our efforts can be efficiently scaled, disseminated, and applied to the opioid and other societal problems such as infant mortality, crime, and natural disasters. This project fits within NSF's mission to promote the progress of science (contribute to the science and engineering of large socially relevant cyberinfrastructures) to advance the health and welfare of US citizens (by linking data sources in new and useful ways to empower communities to address societal problems; establishing sustainable partnerships between academia, industry, government and communities; increasing data literacy and community engagement with data science; and enhancing research and education via development/adaptation of training modules and courses in data analytics).The main goal of this project is to help small and rural communities in the Midwest address the opioid epidemic via BIGDATA (BD) technology. While no communities have been spared, small and rural communities face unique challenges in confronting the opioid epidemic: knowledge and data exist in siloes across multiple organizations with varying jurisdictional boundaries; efforts to collect, link, and analyze data are hampered by a lack of infrastructure and tools; rural areas are plagued by "dead zones" in cellular connectivity; communities lack capacity for data collection, and analytics; needs and resources across effected communities are not uniform and require BD approaches that are flexible, open, leverage significant community input, and can be dutifully validated. Our proposed solution is OpenOD, a framework that provides uniform, relevant and timely access to data. Working integrally with the Midwest Big Data Hub (MBDH) and our partners, our three main objectives are to: (1) Work with local communities to understand strengths and gaps in cyberinfrastructure, data availability, and need for data analytics workforce skills. (2) Assemble flexible cyberinfrastructure that includes a data commons, stakeholder-usable and cloud-amenable data analytics and visualization tools, and internet connectivity with both mobile and non-mobile capabilities. (3) Validate, evaluate, and disseminate cyberinfrastructure and data analytics tools to stakeholder groups throughout the region while fostering new partnerships. OpenOD will create approaches that will allow governing units to deploy openly available tools rather than rely on proprietary tools. In this way, existing disparities in data access and ensuing responses are effectively addressed. The potential contributions of the project are to: (1) Increase BD and STEM literacy and community engagement in underrepresented groups given the operating milieu of OpenOD in rural areas where the population is indigent and lacks adequate skills to join the modern workforce. (2) Improve well-being of individuals in society by linking data sources in new and useful ways to empower communities to address the opioid crisis; improved connectivity and timely delivery of critical information will accelerate community responsiveness and improve preventive strategies. (3) Provide infrastructure for research and education will be improved given that project activities will deliver linked, curated data sets to community stakeholders, researchers and educators. Training modules and courses adapted and developed and shared with local/regional educators and will remain with the communities after the funding period has ended. In addition, new and established partnerships will allow sustainability of the project in the communities for the long-term.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.

阿片类药物危机破坏俄亥俄州，中西部不成比例地影响小型和农村社区。利用和部署数据有望在政策制定者，医疗保健提供者和社区公民对这一危机的回应中发展。当前，将数据掌握在前线的个人手中有许多障碍。关键数据在执法部门，公共卫生部门，医院和诊所以及县政府中孤立；数据通常是在不同机构和部门的非标准方式不准确或以非标准的方式收集的；药物滥用的污名限制了与药物有关的死亡的准确报告；由于缺乏隐私和安全框架，因此与社区和其他利益相关者没有共享信息。例如，这样的障碍可以防止成瘾的人或其家人和朋友找到可用的治疗中心或及时获得其他重要信息。同样，急救人员和医疗保健提供者很难获得关键的最新信息。在主要是农村县，这些挑战尤其令人生畏，因为连通性和沟通基础设施通常较差。这个大数据讲话项目涉及开发可扩展，灵活和连通性的数据驱动方法来解决阿片类药物流行。网络基础设施框架（OpenOD）最初是在俄亥俄州阿巴拉契亚州和中西部的小型和农村社区中设计和部署的，那里的数据和连接需求最大。基于重要的社区投入，OpenOD还将创建最终用户应用程序或企业解决方案，以支持利益相关者和社区，以实现他们认为在当地一级最有效和最有益的响应。当与NSF的中西部大数据中心交谈时，我们的努力可以有效地扩展，传播并应用于阿片类药物和其他社会问题，例如婴儿死亡率，犯罪和自然灾害。该项目符合NSF促进科学进步的使命（有助于大型社会相关的网络基础设施的科学和工程），以促进美国公民的健康和福利（通过以新的和有用的方式将数据源联系起来，以授权社区授权社区来解决社会问题，以解决社会问题，通过与政府，政府，政府，政府，政府，政府，社区之间建立可持续性；数据分析中的培训模块和课程的开发/适应）。该项目的主要目标是通过Bigdata（BD）技术帮助中西部的小型和农村社区解决阿片类药物流行。尽管没有幸免的社区，但小型和农村社区在面对阿片类药物流行方面面临着独特的挑战：在多个具有不同管辖权边界的组织中，知识和数据存在；缺乏基础架构和工具来收集，链接和分析数据的努力受到阻碍。农村地区在细胞连通性中受到“死区”的困扰。社区缺乏收集数据和分析的能力；跨影响社区的需求和资源不是统一的，需要灵活，开放，利用重要社区投入的BD方法，并且可以忠实地验证。我们提出的解决方案是OpenOD，该框架可提供统一，相关和及时访问数据的框架。与中西部大数据中心（MBDH）和我们的合作伙伴的整体合作，我们的三个主要目标是：（1）与当地社区合作，了解网络基础设施的优势和差距，数据可用性以及数据分析劳动力技能的需求。 （2）组装灵活的网络基础结构，其中包括数据共同体，利益相关者使用和云无限的数据分析和可视化工具，以及具有移动和非移动功能的Internet连接性。 （3）验证，评估，评估和传播网络基础设施和数据分析工具，以在整个地区的利益相关者群体中，同时促进新的合作伙伴关系。 OpenOD将创建允许管理单位公开可用工具而不是依靠专有工具的方法。这样，有效解决了数据访问和随后的响应的现有差异。该项目的潜在贡献是：（1）鉴于在人口贫穷的农村地区的开放园区的运营环境，增加了代表性不足的群体的BD和STEM识字率和社区参与，在那里人口贫穷，缺乏足够的技能来加入现代劳动力。 （2）通过将数据源联系起来，以新的和有用的方式将社区授权解决阿片类药物危机的福祉来改善社会中个人的福祉；改善连接性和及时提供关键信息将加速社区响应能力并改善预防策略。 （3）鉴于项目活动将向社区利益相关者，研究人员和教育工作者提供链接的，精心策划的数据集，为研究和教育提供基础设施。培训模块和课程适应，开发并与当地/地区教育者共享，并将在资金期结束后留在社区。此外，新的和已建立的合作伙伴关系将允许该项目在Longterm的社区中的可持续性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持的。

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• DOI: 10.3233/shti220048
• 发表时间: 2022-06
• 期刊: Studies in health technology and informatics
• 作者: Goonmeet Bajaj;Ugur Kursuncu;Manas Gaur;Usha Lokala;A. Hyder;Srinivas Parthasarathy;Amit P. Sheth;Srinivasan Parthasa-rathy

A Computational Approach to Understand Mental Health from Reddit: Knowledge-Aware Multitask Learning Framework

Reddit 上了解心理健康的计算方法：知识感知多任务学习框架

• DOI: 10.1609/icwsm.v16i1.19322
• 发表时间: 2022
• 期刊: Proceedings of the International AAAI Conference on Web and Social Media
• 作者: Lokala, Usha;Srivastava, Aseem;Dastidar, Triyasha Ghosh;Chakraborty, Tanmoy;Akhtar, Md Shad;Panahiazar, Maryam;Sheth, Amit
• 通讯作者: Sheth, Amit