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BMC Public Health
Published in: BMC Public Health 1/2016

Open Access 01-12-2016 | Research article

Text mining for identifying topics in the literatures about adolescent substance use and depression

Authors: Shi-Heng Wang, Yijun Ding, Weizhong Zhao, Yung-Hsiang Huang, Roger Perkins, Wen Zou, James J. Chen

Published in: BMC Public Health | Issue 1/2016

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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.
Appendix
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Literature
1.
Englund MM, Egeland B, Oliva EM, Collins WA. Childhood and adolescent predictors of heavy drinking and alcohol use disorders in early adulthood: a longitudinal developmental analysis. Addiction. 2008;103:23–35.CrossRefPubMedPubMedCentral
2.
3.
Van Ryzin MJ, Fosco GM, Dishion TJ. Family and peer predictors of substance use from early adolescence to early adulthood: an 11-year prospective analysis. Addict Behav. 2012;37:1314–24.CrossRefPubMedPubMedCentral
4.
Tandon DS, Solomon BS. Risk and protective factors for depressive symptoms in urban African American adolescents. Youth Soc. 2009;41:80–99.CrossRef
5.
Goldstein BI, Shamseddeen W, Spirito A, Emslie G, Clarke G, Wagner KD, et al. Substance use and the treatment of resistant depression in adolescents. J Am Acad Child Psy. 2009;48:1182–92.CrossRef
6.
7.
Kaminer Y, Connor DF, Curry JF. Comorbid adolescent substance use and major depressive disorders: a review. Psychiat. 2007;4:33–43.
8.
Townsend AL, Biegel DE, Ishler KJ, Wieder B, Rini A. Families of persons with substance use and mental disorders: a literature review and conceptual framework*. Fam Relat. 2006;55:473–86.CrossRef
9.
Brady KT, Sinha R. Co-occurring mental and substance use disorders: the neurobiological effects of chronic stress. Am J Psychiat. 2005;162:1483–93.CrossRefPubMed
10.
Goodman E, Capitman J. Depressive symptoms and cigarette smoking among teens. Pediatrics. 2000;106:748–55.CrossRefPubMed
11.
Hallfors DD, Waller MW, Bauer D, Ford CA, Halpern CT. Which comes first in adolescence—sex and drugs or depression? Am J Prev Med. 2005;29:163–70.CrossRefPubMed
12.
Measelle JR, Stice E, Hogansen JM. Developmental trajectories of co-occurring depressive, eating, antisocial, and substance abuse problems in female adolescents. J Abnorm Child Psych. 2006;115:524–38.CrossRef
13.
Needham BL. Gender differences in trajectories of depressive symptomatology and substance use during the transition from adolescence to young adulthood. Soc Sci Med. 2007;65:1166–79.CrossRefPubMed
14.
Pang RD, Farrahi L, Glazier S, Sussman S, Leventhal AM. Depressive symptoms, negative urgency and substance use initiation in adolescents. Drug Alcohol Depen. 2014;144:225–30.CrossRef
15.
Ramage D, Rosen E, Chuang J, Manning CD, McFarland DA. Topic modeling for the social sciences. In: NIPS 2009 Workshop on Applications for Topic Models: Text and Beyond. 2009.
16.
O’Mara-Eves A, Thomas J, McNaught J, Miwa M, Ananiadou S. Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst Rev. 2015;4: doi:10.​1186/​2046-4053-4-5.
17.
Holzinger A, Schantl J, Schroettner M, Seifert C, Verspoor K. Biomedical text mining: state-of-the-art, open problems and future challenges. In: Interactive Knowledge Discovery and Data Mining in Biomedical Informatics. Berlin: Springer; 2014. p. 271–300.
18.
Wiedemann G. Opening up to big data: Computer-assisted analysis of textual data in social sciences. Hist Soc Res. Vol. 38, No. 4 (146), 2013:332–357.
19.
Zhu F, Patumcharoenpol P, Zhang C, Yang Y, Chan J, Meechai A, et al. Biomedical text mining and its applications in cancer research. J Biomed Inform. 2013;46:200–11.CrossRefPubMed
20.
Cohen AM, Hersh WR. A survey of current work in biomedical text mining. Brief Bioinform. 2005;6:57–71.CrossRefPubMed
21.
Zhou D, He Y. Extracting interactions between proteins from the literature. J Biomed Inform. 2008;41:393–407.CrossRefPubMed
22.
Jensen LJ, Saric J, Bork P. Literature mining for the biologist: from information retrieval to biological discovery. Nat Rev Genet. 2006;7:119–29.CrossRefPubMed
23.
24.
Swanson DR. Fish oil, Raynaud’s syndrome, and undiscovered public knowledge. Perspect Biol Med. 1986;30:7–18.CrossRefPubMed
25.
Swanson DR: Complementary structures in disjoint science literatures. In: Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval. ACM 1991: 280–9.
26.
27.
28.
Bisgin H, Liu Z, Fang H, Xu X, Tong W. Mining FDA drug labels using an unsupervised learning technique-topic modeling. BMC bioinformatics. 2011;12:S11.CrossRefPubMedPubMedCentral
29.
Yu K, Zhang J, Chen M, Xu X, Suzuki A, Ilic K, et al. Mining hidden knowledge for drug safety assessment: topic modeling of LiverTox as a case study. BMC bioinformatics. 2014;15:S6.CrossRef
30.
31.
32.
Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res. 2003;3:993–1022.
33.
Wang V, Xi L, Enayetallah A, Fauman E, Ziemek D. GeneTopics-interpretation of gene sets via literature-driven topic models. BMC Syst Biol. 2013;7:1.CrossRef
34.
Lehrer JA, Shrier LA, Gortmaker S, Buka S. Depressive symptoms as a longitudinal predictor of sexual risk behaviors among US middle and high school students. Pediatrics. 2006;118:189–200.CrossRefPubMed
35.
Reingle JM, Staras SA, Jennings WG, Branchini J, Maldonado-Molina MM. The relationship between marijuana use and intimate partner violence in a nationally representative, longitudinal sample. J Interpers Violence. 2012;27:1562–78.CrossRefPubMedPubMedCentral
36.
Ruback RB, Clark VA, Warner C. Why Are crime victims at risk of being victimized again? Substance use, depression, and offending as mediators of the victimization–revictimization link. J Interpers Violence. 2013;29:157–85.CrossRefPubMed
37.
Pesola F, Shelton KH, Bree M. Sexual orientation and alcohol problem use among UK adolescents: an indirect link through depressed mood. Addiction. 2014;109:1072–80.CrossRefPubMed
38.
39.
Kaukinen C, DeMaris A. Age at first sexual assault and current substance use and depression. J Interpers Violence. 2005;20:1244–70.CrossRefPubMed
40.
Mackie CJ, Castellanos‐Ryan N, Conrod PJ. Personality moderates the longitudinal relationship between psychological symptoms and alcohol use in adolescents. Alcohol Clin Exp Res. 2011;35:703–16.CrossRefPubMed
41.
Edwards AC, Heron J, Dick DM, Hickman M, Lewis G, MacLeod J, et al. Adolescent alcohol use is positively associated with later depression in a population-based UK cohort. J Stud Alcohol Drugs. 2014;75:758–65.CrossRefPubMedPubMedCentral
42.
Sihvola E, Rose RJ, Dick DM, Pulkkinen L, Marttunen M, Kaprio J. Early‐onset depressive disorders predict the use of addictive substances in adolescence: a prospective study of adolescent Finnish twins. Addiction. 2008;103:2045–53.CrossRefPubMedPubMedCentral
43.
McCarty CA, Wymbs BT, Mason WA, King KM, McCauley E, Baer J, et al. Early adolescent growth in depression and conduct problem symptoms as predictors of later substance use impairment. J Abnorm Child Psych. 2013;41:1041–51.CrossRef
44.
McKenzie M, Olsson CA, Jorm AF, Romaniuk H, Patton GC. Association of adolescent symptoms of depression and anxiety with daily smoking and nicotine dependence in young adulthood: findings from a 10‐year longitudinal study. Addiction. 2010;105:1652–9.CrossRefPubMed
45.
Copeland W, Angold A, Shanahan L, Dreyfuss J, Dlamini I, Costello EJ. Predicting persistent alcohol problems: a prospective analysis from the Great Smoky Mountain Study. Psychol Med. 2012;42:1925–35.CrossRefPubMedPubMedCentral
46.
Blei DM, Lafferty JD. Dynamic topic models. In: Proceedings of the 23rd international conference on Machine learning: 2006. ACM 2006: 113–20.
47.
Nutt D, McLellan AT. Can neuroscience improve addiction treatment and policies? Public Health Rev. 2014;35.
48.
Wang X, McCallum A, Wei X. Topical n-grams: Phrase and topic discovery, with an application to information retrieval. In: Data Mining, 2007 ICDM 2007 Seventh IEEE International Conference on: 2007: IEEE; 2007: 697–702.
49.
Wallach HM. Topic modeling: beyond bag-of-words. In: Proceedings of the 23rd international conference on Machine learning: 2006: ACM; 2006: 977–84.
50.
Li W, McCallum A. Pachinko allocation: DAG-structured mixture models of topic correlations. In: Proceedings of the 23rd international conference on Machine learning: 2006: ACM; 2006: 577–84.
51.
Griffiths D, Tenenbaum M. Hierarchical topic models and the nested Chinese restaurant process. Adv Neural Inf Process Syst. 2004;16:17–24.
Metadata
Title
Text mining for identifying topics in the literatures about adolescent substance use and depression
Authors
Shi-Heng Wang
Yijun Ding
Weizhong Zhao
Yung-Hsiang Huang
Roger Perkins
Wen Zou
James J. Chen
Publication date
01-12-2016
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2016
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-016-2932-1

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