摘要
突发监测,是通过观察增长率骤然上升的突发词的发展变化,来探测学科前沿的方法。SemRep可以根据UMLS(unified medical language system)提取自然语言语义关系。本文通过SemRep结合突发监测算法,揭示某领域研究现状及发展趋势,以疾病药物治疗为例,分析了SARS药物治疗领域的研究重点和热点。在新型冠状病毒肺炎疫情背景下,为新型冠状病毒(SARS-CoV-2)防控药物的选择与开发提供有力线索。在SARS药物治疗研究文献集合中,利用SemRep和SemRep语义结果处理系统,根据UMLS语义关系,提取存在治疗关系的药物术语概念集合,合并去重后得到Ribavirin(利巴韦林)等有效概念51个,这些药物是SARS治疗常规药物,主要用于疫情发生时的临床急救。根据Kleinberg突发监测算法,计算药物概念的突发权重指数,将概念按突发权重指数高低排序后,得到SARS治疗潜力药物,这些药物大多是在疫情结束后进行的抗病毒实验室研究。SemRep结合突发监测的方法不仅适用于疾病药物治疗领域,也用于各个学科研究热点的挖掘。
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 Sem‐Rep 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 dupli‐cates 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.
作者
徐爽
许丹
韩爽
杨颖
Xu Shuang;Xu Dan;Han Shuang;Yang Ying(Library of China Medical University,Shenyang 110122)
出处
《情报学报》
CSSCI
CSCD
北大核心
2021年第7期745-755,共11页
Journal of the China Society for Scientific and Technical Information
基金
辽宁省教育厅科学研究项目青年科技人才“育苗”项目人文社科类“pubMR结合突发监测算法预测疾病药物治疗发展趋势”(QNRW2020005)
中国医科大学“青年骨干支持计划”(人文社科类)(A类)项目“基于突发监测的ESI世界前沿科学发展趋势预测”(QGRA2018009)
CALIS全国医学文献信息中心科研基金项目“大数据环境下基于突发监测的医学研究前沿发展趋势预测”(CALIS-2018-02-001)
辽宁省社会科学规划基金项目“大数据驱动下智慧化学科精准服务平台设计与构建”(L20BTQ002)。
作者简介
徐爽,女,1983年生,硕士生导师,副研究馆员,主要研究方向为情报分析与知识发现;许丹,女,1985年生,硕士生导师,副研究馆员,主要研究方向为文献计量学;韩爽,女,1985年生,硕士,副研究馆员,主要研究方向为医学信息学;杨颖,女,1980年生,硕士生导师,研究馆员,主要研究方向为数据挖掘,E-mail:yingyang80@126.com。
