Article ; Online: Expanding Our Understanding of COVID-19 from Biomedical Literature Using Word Embedding.
International journal of environmental research and public health
2021 Volume 18, Issue 6
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, ...
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. |
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MeSH term(s) | Artificial Intelligence ; COVID-19 ; Coronavirus Infections ; Humans ; Machine Learning ; SARS-CoV-2 |
Language | English |
Publishing date | 2021-03-15 |
Publishing country | Switzerland |
Document type | Journal Article ; Research Support, Non-U.S. Gov't |
ISSN | 1660-4601 |
ISSN (online) | 1660-4601 |
DOI | 10.3390/ijerph18063005 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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