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

Authors Xu S, Xu D, Wen L, Zhu C, Yang Y, Han S, Guan P

Received 2 July 2020

Accepted for publication 7 September 2020

Published 24 September 2020 Volume 2020:14 Pages 3899—3913

DOI https://doi.org/10.2147/DDDT.S270379

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Manfred Ogris


Shuang Xu,1 Dan Xu,1 Liang Wen,2 Chen Zhu,3 Ying Yang,1 Shuang Han,1 Peng Guan4

1School of Library and Medical Informatics, China Medical University, Shenyang, Liaoning, People’s Republic of China; 2Department of Neurosurgery, The General Hospital of Shenyang Military Command, Shenyang, Liaoning, People’s Republic of China; 3Department of Neurosurgery, The First Hospital of China Medical University, Shenyang, Liaoning, People’s Republic of China; 4Department of Epidemiology, School of Public Health, China Medical University, Shenyang, Liaoning, People’s Republic of China

Correspondence: Peng Guan
Department of Epidemiology, School of Public Health, China Medical University, Shenyang 110122, Liaoning, People’s Republic of China
Email pguan@cmu.edu.cn

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.

Keywords: post-traumatic stress disorder, burst detection, Kleinberg’s algorithm, burst word, Unified Medical Language System, SemRep

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