SCC-PG: Using Innovations in Sensing, Data Analytics, and Community Engagement to Address Opioid Overdose Crisis

SCC-PG：利用传感、数据分析和社区参与方面的创新来解决阿片类药物过量危机

• 批准号: 2125430
• 负责人: Sherif Abdelwahed
• 金额: $ 14.88万
• 依托单位: Virginia Commonwealth University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125430&HistoricalAwards=false
• 关键词: SCC; PG; Using; Innovations; Sensing

Opioid overdose is now the leading cause of death for those under 50 in the USA. Cities have followed different strategies to address this problem through various education/training programs. However, the growing scale of the opioid overdose crisis in the USA indicates that more effective data-driven approaches are needed. Opioid abuse and overdose have been identified as a leading method of premature death in the Richmond Region, Virginia. Inadequate data is a major issue for city officials, which prevents them from investigating the scale of the opioid epidemic. Since locality’s economic competitiveness and their ability to recruit businesses and labor are dependent on the image they portray, the data sharing and further analytics in opioid overdoses have seen limited improvement. To this end, this planning project proposes to build a team of researchers and local stakeholders - including those in the leadership roles in all cities and counties in the Richmond Region. The team will work towards a data-driven understanding of the problem and community-involved solutions to address the issue. As responses to the opioid problem are common to other metro regions in the U.S., we hope to scale the model to be exercised in other regions. This project aims to harness the power of data analytics and smart technologies to develop creative solutions for efficient decision-making and planning to improve public health and living standards. Direct broader impacts include evidence-based factors that enhance and impair community responses to the opioid epidemic that can be discussed and refined with community members to drive change, and accordingly, reduce opioid overdose and health inequities across the region.From the technical perspectives, this project will investigate novel data-driven approaches to treatment policies that can be supported by the community. The intellectual merit of this work includes: (1) developing a fundamental understanding of challenges facing communities due to opioid epidemics, (2) developing a better understanding of the relationship between governance, smart cities, and social innovation, particularly for addressing the opioid problem, (3) collecting relevant types of drug use data in the community, (4) deriving prediction models based on the available data from different sources, and (5) developing smart sensing solutions to accurately monitor and assess the state of drug abuse. Accordingly, this project aims to establish interdisciplinary efforts to develop a data-driven intervention in addressing the opioid overdose crisis, in coordination with community representatives to identify and assess applicable approaches. The team will investigate several predictive models for forecasting drug use/overdoses by considering diverse data on drug-related incidents. The project will also investigate Explainable Machine Learning techniques that will be coupled with developed data-driven models in order to provide estimations of drug use with an explanation of the important factors that justify the predictions made by the model. This will help identify the root causes and the extent of their impact.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.

阿片类药物过量现在是美国50岁以下的人的主要死亡原因。城市已经遵循不同的策略，通过各种教育/培训计划来解决此问题。但是，美国阿片类药物过量危机的日益扩展表明需要更有效的数据驱动方法。在弗吉尼亚州里士满地区，阿片类药物滥用和过量用药被确定为早产的主要方法。对于城市官员来说，数据不足是一个主要问题，这阻止了他们研究阿片类药物流行的规模。由于当地的经济竞争力及其招募企业和劳动力的能力取决于它们所描绘的形象，因此阿片类药物过量服用的数据共享和进一步的分析有限。为此，这项计划项目的建议旨在建立一个研究人员和当地利益相关者，包括里士满地区所有城市和县的领导职务。该团队将致力于对问题和社区涉及解决方案的数据驱动理解，以解决该问题。由于对美国其他地铁地区对阿片类药物问题的反应是在其他地区进行的。该项目旨在利用数据分析和智能技术的力量，以开发有效决策和计划以提高公共卫生和生活水平的创造性解决方案。直接广播公司的影响包括基于证据的因素，可以增强和损害社区对阿片类药物流行病的反应，可以与社区成员讨论和完善社区成员以驱动变革，并减少阿片类药物的过量和健康不平等。从技术角度来看，从技术角度来看，该项目将调查可以由社区支持的新颖数据驱动方法来支持社区。这项工作的智力优点包括：（1）对由于阿片类型事件引起的社区面临的挑战发展基本理解，（2）对治理，智能城市和社会创新之间的关系有了更好的了解，尤其是针对阿片类药物问题，尤其是针对社区中的智能数据，以及（4）从智能数据中收集不同的预测类型，（4）（4）（4）（4）（4）评估药物滥用状态。根据该项目的说法，旨在建立跨学科的努力，以与代表和评估适用方法的社区协调，以解决阿片类药物过量危机以解决阿片类药物过量危机。该团队将通过考虑潜水员在与药物有关的事件上的数据进行研究，以研究一些预测药物使用/过量的预测模型。该项目还将调查可解释的机器学习技术，这些技术将与开发的数据驱动模型相结合，以提供对药物使用的估算，并解释重要因素，以证明该模型的预测是合理的。这将有助于确定根本原因及其影响的程度。该奖项反映了NSF的法定使命，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估，被认为是宝贵的支持。