title: Text mining for identifying topics in the literatures about adolescent substance use and depression.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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 of topics and demonstrate the distribution of vocabularies over each topic.
  
  
  RESULTS
  The LDA topics recaptured the search keywords in PubMed, and further discovered relevant issues, such as intervention program, association links between adolescent substance use and adolescent depression, such as sexual experience and violence, and risk factors of adolescent substance use, such as family factors and peer networks. Using trend analysis to explore the dynamics of proportion of topics, we found that brain research was assessed as a hot issue by the coefficient of the trend test.
  
  
  CONCLUSIONS
  Topic modeling has the ability to segregate a large collection of articles into distinct themes, and it could be used as a tool to understand the literature, not only by recapturing known facts but also by discovering other relevant topics.
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|Graduate Institute of Biostatistics, China Medical University, No. 91, Xueshi Rd, Taichung City, 40402, Taiwan.
authors.forename: Shi-Heng
authors.initials: SH
authors.lastname: Wang
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.
authors.forename: Yijun
authors.initials: Y
authors.lastname: Ding
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.
authors.forename: Weizhong
authors.initials: W
authors.lastname: Zhao
authors.affiliation: National Applied Research Laboratories, National Center for High-Performance Computing, No. 7, R&D 6th Rd., Hsinchu Science Park, Hsinchu City, 30076, Taiwan.
authors.forename: Yung-Hsiang
authors.initials: YH
authors.lastname: Huang
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.
authors.forename: Roger
authors.initials: R
authors.lastname: Perkins
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA. wen.zou@fda.hhs.gov.
authors.forename: Wen
authors.initials: W
authors.lastname: Zou
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA. jamesj.chen@fda.hhs.gov.
authors.forename: James J
authors.initials: JJ
authors.lastname: Chen
mesh_terms: D000293:Adolescent; D000294:Adolescent Behavior; D057225:Data Mining / Q000379:methods*; D003863:Depression / Q000453:epidemiology*; D006801:Humans; D019966:Substance-Related Disorders / Q000453:epidemiology*
keywords: Adolescent; Depression; Substance use; Text mining; Topic model