A Method of Identifying Treatment Modalities Related to COVID-19 from PubMed Abstracts

In the past three to four years, COVID-19, a highly contagious disease caused by a novel coronavirus, has resulted in a significant number of fatalities. Numerous interventions have been carried out in an effort to preserve human life. Despite the current decline in the COVID-19 outbreak, the virus responsible for the disease has undergone a significant mutation, resulting in modifications to treatment protocols. As viruses endure mutations, disease severity tends to increase. Therefore, prior expertise treating patients is beneficial for diagnosing the disease and determining an effective treatment plan. This is the objective of this study, which proposes a text mining approach for identifying relevant information regarding COVID-19 treatment modalities in clinical trials. Constraint-based k-means clustering is the principal algorithm utilized in the proposed method. The datasets utilized in this study are PubMed abstracts related to COVID-19 in clinical trials. Three medical professionals will provide a ground truth for the dataset used in the study. Upon conducting an evaluation of the outcomes using the recall score, it is possible to conclude that the proposed methodology produced results that are deemed satisfactory.