Text mining for identifying topics in the literatures about adolescent substance use and depression
作者: Shi-Heng WangYijun DingWeizhong ZhaoYung-Hsiang HuangRoger PerkinsWen ZouJames J. Chen
作者单位: 1Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration
2China Medical University
3National Applied Research Laboratories, National Center for High-Performance Computing
刊名: BMC Public Health, 2016, Vol.16 (1)
来源数据库: Springer Nature Journal
DOI: 10.1186/s12889-016-2932-1
关键词: Topic modelText miningAdolescentSubstance useDepression
英文摘要: Abstract(#br) Background(#br)Both adolescent substance use and adolescent depression are major public health problems, and have the tendency to co-occur. Thousands of articles on adolescent substance use or depression have been published. It is labor intensive and time consuming to extract huge amounts of information from the cumulated collections. Topic modeling offers a computational tool to find relevant topics by capturing meaningful structure among collections of documents.(#br) Methods(#br)In this study, a total of 17,723 abstracts from PubMed published from 2000 to 2014 on adolescent substance use and depression were downloaded as objects, and Latent Dirichlet allocation (LDA) was applied to perform text mining on the dataset. Word clouds were used to visually display the content...
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影响因子:2.076 (2012)

  • adolescent 青年
  • depression 低地
  • substance 物质
  • identifying 识别
  • mining 矿业
  • about 大约