An IR-based QA System for Impact of Social Determinants of Health on Covid-19

Question Answering (QA), a branch of Natural Language Processing (NLP), automates information retrieval of answers to natural language questions from databases or documents without human intervention. Motivated by the COVID-19 pandemic and the increasing awareness of Social Determinants of Health (SDoH), we built a prototype QA system that combines NLP, semantics, and IR systems with the focus on SDoH and COVID-19. Our goal was to demonstrate how such technologies could be leveraged to allow decision-makers to retrieve answers to queries from very large databases of documents. We used documents from CORD-19 and PubMed datasets, merged the COVID-19 (CODO) ontology with published ontologies for homelessness and gender, and used the mean average precision metric to evaluate the system. Given the interdisciplinary nature of this research, we provide details of the methodologies used. We anticipate that QA systems can play a significant role in providing information leading to improved health outcomes.