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| Application of SemRep and Burst Detection Algorithm in Bibliometric Analysis—A Case Study on the Development Trend of Drug Therapy |
| Xu Shuang, Xu Dan, Han Shuang, Yang Ying |
| Library of China Medical University, Shenyang 110122 |
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Abstract Burst detection is a method of detecting frontiers in science by observing the development and changes in burst words, that is, words with a sharp increase in growth rate. SemRep extracts natural language semantic relationships based on Unified Medical Language System (UMLS). This paper reveals the research status and development trend of a field through SemRep combined with the burst detection algorithm, and analyzes the research focus and hotspots in SARS drug therapy research. In the context of the COVID-19 outbreak, this study provides a strong lead for the selection and development of drugs for SARS-CoV-2 prevention and control. Using the dataset on drug therapy of SARS, the SemRep and SemRep semantic processing system were used to extract the drug terminology concept sets with “TREAT” relationship according to the UMLS semantic relationship. Fifty-one effective concepts such as “Ribavirin” were obtained after duplicates were removed. These drugs were routine medications for SARS. They were mainly used for clinical first aid in the event of an outbreak. According to the Kleinberg’s burst detection algorithm, the burst weight index of the drug concepts was calculated. The potential drugs for SARS were obtained by sorting the concepts according to the burst weight index. Most of these drugs were studied in antiviral laboratories after the outbreak was curbed. The method of SemRep combined with burst detection is not only applicable to the field of drug therapy for diseases but also in identifying research hotspots in various disciplines.
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Received: 14 May 2020
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