Machine Learning Techniques During the COVID-19 Pandemic: A Bibliometric Analysis

Abstract

The Coronavirus pandemic (COVID-19) has encouraged researchers to produce significant scientific research in this field in reputable international citation databases. It is important to constantly identify and assess scientific outputs in order to learn more about the situation. One of the methods used for evaluating scientific research activities is scientometrics, which has many applications in describing, explaining and predicting the scientific status of researchers and research centers in various national and international fields. It also provides efficient methods for monitoring and ranking organizations, researchers, journals and countries. On the other hand, in recent years, the use of various scientometric techniques, including co-word analysis, co-authorship network and scientific network, has been of great help in discovering the direction of researchers' production in scientific domain and its hidden and overt dimensions. One of the most popular areas since the COVID-19 epidemic started, has been research the use of artificial intelligence and especially machine learning techniques in the prediction, diagnosis and treatment of this disease. In this regard, 2659 documents from the PubMed citation database since the start of the COVID-19 epidemic have been reviewed. The findings of this research show that America, China, India and England are the countries that have cooperated the most with other countries. In addition, the results of this research showed that deep learning and CNN had been significantly used in the researchers' studies.