SBIR Phase I (COVID-19): Identifying Medical Supply Shortages on Social Media for Fast and Effective Disaster Response

这个小企业创新研究第一阶段项目的更广泛影响包括通过确定医疗提供者的需求和为政府机构、医疗设备供应商和制造商编写报告，在新冠肺炎危机期间提供即时帮助。拟议的自然语言处理方法将帮助(1)医院和诊所寻求医疗用品、个人防护设备和测试用品以满足其需求；(2)政府协调响应；(3)制造商和供应商寻找有关需求的信息。这个小型企业创新研究(SBIR)第一阶段项目将利用自然语言处理和机器学习方面的最新进展，根据从社交媒体上的自由文本获得的见解，大规模识别医疗设备和用品的需求，并将这些需求转换为集中的、易于访问的结构化数据格式。这项技术将识别社交媒体上的需求表达；识别用户、他们的特定需求和位置；并生成按地理位置排序的可操作格式列表。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase I project consists of providing immediate help during the COVID-19 crisis by identifying the needs of medical providers and compiling reports for government agencies and medical equipment suppliers and manufacturers. The proposed Natural Language Processing methodology will help (1) hospitals and clinics seeking medical supplies, personal protective equipment, and testing supplies to meet their needs; (2) the government coordinating response; (3) manufacturers and suppliers seeking information regarding needs. Additionally, it can be used to identify other non-medical supply shortages and can be adapted to provide an efficient response for other disasters or outbreaks.This Small Business Innovation Research (SBIR) Phase I project will leverage recent advances in natural language processing and machine learning to identify at scale needs in medical equipment and supplies, based on insights derived from free text in social media, and convert these needs into a centralized, easily accessible structured data format. The technology will identify expressions of needs on social media; identify users, their specific needs, and locations; and generate geographically sorted actionable formatted lists.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.