Expanding Our Understanding of COVID-19 from Biomedical Literature Using Word Embedding

• Published in: International journal of environmental research and public health Vol. 18; no. 6; p. 3005
• Main Authors: Yang, Heyoung, Sohn, Eunsoo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 15.03.2021; MDPI
• Subjects: Artificial Intelligence; Bibliometrics; Coronavirus Infections; Coronaviruses; COVID-19; COVID-19 vaccines; Dictionaries; Disease; Drugs; Humans; Keywords; Knowledge discovery; Machine Learning; Medical Subject Headings-MeSH; Proteins; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; Subject heading schemes; Vaccines; Viruses; France; COVID-19; PubMed literature; drug repurposing; word embedding; substance name; machine learning; medical subject headings
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph18063005

A better understanding of the clinical characteristics of coronavirus disease 2019 (COVID-19) is urgently required to address this health crisis. Numerous researchers and pharmaceutical companies are working on developing vaccines and treatments; however, a clear solution has yet to be found. The current study proposes the use of artificial intelligence methods to comprehend biomedical knowledge and infer the characteristics of COVID-19. A biomedical knowledge base was established via FastText, a word embedding technique, using PubMed literature from the past decade. Subsequently, a new knowledge base was created using recently published COVID-19 articles. Using this newly constructed knowledge base from the word embedding model, a list of anti-infective drugs and proteins of either human or coronavirus origin were inferred to be related, because they are located close to COVID-19 on the knowledge base. This study attempted to form a method to quickly infer related information about COVID-19 using the existing knowledge base, before sufficient knowledge about COVID-19 is accumulated. With COVID-19 not completely overcome, machine learning-based research in the PubMed literature will provide a broad guideline for researchers and pharmaceutical companies working on treatments for COVID-19.