<p>Integrating Unified Medical Language System and Kleinberg’s Burst Detection Algorithm into Research Topics of Medications for Post-Traumatic Stress Disorder</p>

Abstract

Background: The treatment of post-traumatic stress disorder (PTSD) has long been a challenge because the symptoms of PTSD are multifaceted. PTSD is primarily treated with psychotherapy and medication, or a combination of psychotherapy and medication. The present study was designed to analyze the literature on medications for PTSD and explore high-frequency common drugs and low-frequency burst drugs by burst detection algorithm combined with Unified Medical Language System (UMLS) and provide references for developing new drugs for PTSD. Methods: Publications related to medications for PTSD from 2010 to 2019 were identified through PubMed, Web of Science Core Collection, and BIOSIS Previews. SemRep and SemRep semantic result processing system were performed to extract the set of drug concepts with therapeutic relationship according to the semantic relationship of UMLS. Kleinberg's burst detection algorithm was applied to calculate the burst weight index of drug concepts by a Java-based program. These concepts were sorted according to the frequency and the burst weight index. Results: Four hundred and fifty-nine treatment-related drug concepts were extracted. The drug with the highest burst weight index was "Psilocybine", a hallucinogen, which was more likely to be a hotspot for the pharmacotherapy of PTSD. The highest frequency concept was "prazosin", which was more likely to be the focus of research in the medications for PTSD. Conclusion: The present study assessed the medication-related literature on PTSD treatment, providing a framework of burst words detection-based method, a baseline of information for future research and the new attempt for the discovery of textual knowledge. The bibliometric analysis based on the burst detection algorithm combined with UMLS has shown certain feasibility in amplifying the microscopic changes of a specific research direction in a field, it can also be used in other aspects of disease and to explore the trends of various disciplines.

Mapping Research Trends of Medications for Multidrug-Resistant Pulmonary Tuberculosis Based on the Co-Occurrence of Specific Semantic Types in the MeSH Tree: A Bibliometric and Visualization-Based Analysis of PubMed Literature (1966–2020)

“…The resulting burst drugs are more specific and have more practical meaning than the burst keywords and the burst terms extracted by CiteSpace. 45 This method has a huge amount of computation, which is the next improvement direction of the present study.…”

A Summary on Tuberculosis Vaccine Development—Where to Go?

“…Burst detection is an algorithm developed by Kleinberg [ 34 ]. Kleinberg’s burst detection algorithm is considered a powerful tool to calculate and identify the frontier or emerging trend of research on the timeline.…”

Quantitative study of bioinformatics analysis on glioma: a bibliometric analysis

“…We used VOSviewer software to analyse the cooperation and citation relationships (Figures 3A, B) and identify the top 10 countries with the highest number of publications (Figure 3C; Table 1) and citations (Figure 3D; Table 2). China (2256) has the highest number of publications, followed by the USA (635), Germany (118), India (84), South Korea (69), Taiwan, China (61), Italy (56), England (53), Japan (45), and France (41). The USA has the highest number of citations (30,711), followed by China (30,109), Australia (6553), Belgium (6162), Germany (4626), Switzerland (2451), South Korea (1457), Italy (1254), and Israel (1237).…”

“…The burst detection algorithm was invented by Kleinberg (41), and its calculation results can reflect the frontier and development trends of research over time. We used this algorithm to determine the key references related to glioma bioinformatics research.…”