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Award Abstract # 1761931
Spokes: MEDIUM: MIDWEST: Collaborative: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: WRIGHT STATE UNIVERSITY
Initial Amendment Date: August 27, 2018
Latest Amendment Date: August 27, 2018
Award Number: 1761931
Award Instrument: Standard Grant
Program Manager: Wendy Nilsen
wnilsen@nsf.gov
 (703)292-2568
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 1, 2018
End Date: November 30, 2019 (Estimated)
Total Intended Award Amount: $120,000.00
Total Awarded Amount to Date: $120,000.00
Funds Obligated to Date: FY 2018 = $0.00
History of Investigator:
  • Amit Sheth (Principal Investigator)
    amit@sc.edu
Recipient Sponsored Research Office: Wright State University
3640 COLONEL GLENN HWY
DAYTON
OH  US  45435-0002
(937)775-2425
Sponsor Congressional District: 10
Primary Place of Performance: Wright State University
3640 Colonel Glenn Highway
Dayton
OH  US  45435-0001
Primary Place of Performance
Congressional District:
10
Unique Entity Identifier (UEI): NPT2UNTNHJZ1
Parent UEI:
NSF Program(s): BD Spokes -Big Data Regional I
Primary Program Source: 01001819DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 028Z
Program Element Code(s): 024Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Bajaj, Goonmeet and Kursuncu, Ugur and Gaur, Manas and Usha Lokala, Manas and Hyder, Ayaz and Parthasarathy, Srinivasan and Sheth, Amit "Knowledge-Driven Drug-Use NamedEntity Recognition with Distant Supervision" Studies in health technology and informatics , 2022 Citation Details
Lokala, Usha and Lamy, Francois and Daniulaityte, Raminta and Gaur, Manas and Gyrard, Amelie and Thirunarayan, Krishnaprasad and Kursuncu, Ugur and Sheth, Amit "Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study" JMIR Public Health and Surveillance , v.8 , 2022 https://doi.org/10.2196/24938 Citation Details
Lokala, Usha and Srivastava, Aseem and Dastidar, Triyasha and Chakraborty, Tanmoy and Akhtar, Md. Shad and Panahiazar, Maryam and Sheth, Amit "A Computational Approach to Understand Mental Health from Reddit: Knowledge-Aware Multitask Learning Framework." Sixteenth International AAAI Conference onWeb and Social Media (ICWSM 2022) , 2022 Citation Details
Lokala, Usha and Srivastava, Aseem and Dastidar, Triyasha Ghosh and Chakraborty, Tanmoy and Akhtar, Md Shad and Panahiazar, Maryam and Sheth, Amit "A Computational Approach to Understand Mental Health from Reddit: Knowledge-Aware Multitask Learning Framework" Proceedings of the International AAAI Conference on Web and Social Media , v.16 , 2022 https://doi.org/10.1609/icwsm.v16i1.19322 Citation Details
Roy, Kaushik and Gaur, Manas and Soltani, Misagh and Rawte, Vipula and Kalyan, Ashwin and Sheth, Amit "ProKnow: Process knowledge for safety constrained and explainable question generation for mental health diagnostic assistance" Frontiers in Big Data , v.5 , 2023 https://doi.org/10.3389/fdata.2022.1056728 Citation Details
Roy, Kaushik and Lokala, Usha and Gaur, Manas and Sheth, Amit P "Tutorial: Neuro-symbolic AI for Mental Healthcare" Proceedings of the Second International Conference on AI-ML Systems , 2022 https://doi.org/10.1145/3564121.3564817 Citation Details

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