ABSTRACT
Background: COVID-19 has contributed to healthcare inequity amongst minorities and lower socioeconomic populations, while complicating present anti-cancer treatment regimens. Due to their immunocompromised status, cancer patients are at an increased risk of severe SARS-CoV-2 infection. While sentiment analysis via SM has seen vast growth among healthcare professional, deeper connection and management has been lacking. Given the higher usage of SM impressions and the increase in healthcare disparities especially at the intersection of oncology and COVID-19, the aim of this study was to develop a platform that can: (1) show that the relationships highlighted within these tweets can be realized in biomolecular interactions-specifically within the interaction between solid tumors and COVID-19;(2) use SM data to connect patients with clinical trials. Methods: To determine this relationship, ontologies, which are groupings of terms and related identifiers, such as genes, were created for general search terms, utilizing the Human Phenotype Ontology. They were then combined with “COVID-19” and used as search terms in Twitter's Standard Search tool. The keywords with the most matches were then queried through clinicaltrials.gov and European Bioinformatics Institute's (EBI) Protein Search Tool to find relevant clinical trials and proteins. Finally, the proteins found by the EBI protein search were run through the SwissModel Tool to find relevant protein structures before being used in binding using Polar+'s Binding Platform from Iff Technologies, which provides K values related to 50% inhibition for each medication or immunotherapy. This produced a set of disease-specific keywords that are related to top tweets, clinical trials, protein structures, and binding concentration values in relevant biomolecular pathways for the keyword set “Tumor COVID-19”. Results: The example shown in Table is produced via our platform, with keywords with tweet numbers greater than 95% of all tweets with connected keywords used. Conclusions: By utilizing SM with highly relevant keywords, this platform can combat healthcare inequity by connecting patients and their tweets to clinical trials and enhance literacy about their medical conditions, while providing a greater understanding of the biomolecular pathways involved.