Treatment recommendations for COVID-19 patients along with robust explanations

Abstract

The global response to the pandemic introduced by COVID-19 is unprecedented. Scientists develop methods, which analyze data to identify an effective treatment that uncovers possible responses to the SARS-COV-2 virus. However, our global response should be based on knowledge exchange and collaboration among countries. In this paper, we present a recommender system for treatment recommendations, which exploits similar patterns among patients of different clinical studies, and recommends them health interventions (such as to provide oxygen therapy) and drugs (e.g., Remdesivir) based on their symptoms' or diseases' similarity with patients of other similar clinical studies. Our approach can also provide explanations along with recommended treatments to assist doctors in understanding the reasons behind a suggested drug or health intervention. We also perform experiments to identify the effectiveness of our system in terms of recommendation accuracy. Our results demonstrate that our system is able to minimize the false positive and false negative prediction rates. Finally, we provide web links to download both (i) our program's setup file and (ii) our Neo4j database file.