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

Heyoung Yang and Eunsoo Sohn
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Heyoung Yang: Future Technology Analysis Center, Korea Institute of Science and Technology Information, 66, Hoegi-ro, Dongdaemun-gu, Seoul 02456, Korea
Eunsoo Sohn: Future Technology Analysis Center, Korea Institute of Science and Technology Information, 66, Hoegi-ro, Dongdaemun-gu, Seoul 02456, Korea

IJERPH, 2021, vol. 18, issue 6, 1-18

Abstract: 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.

Keywords: word embedding; machine learning; COVID-19; PubMed literature; drug repurposing; medical subject headings; substance name (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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