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Text Mining of Journal Articles for Sleep Disorder Terminologies

Author

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  • Calvin Lam
  • Fu-Chih Lai
  • Chia-Hui Wang
  • Mei-Hsin Lai
  • Nanly Hsu
  • Min-Huey Chung

Abstract

Objective: Research on publication trends in journal articles on sleep disorders (SDs) and the associated methodologies by using text mining has been limited. The present study involved text mining for terms to determine the publication trends in sleep-related journal articles published during 2000–2013 and to identify associations between SD and methodology terms as well as conducting statistical analyses of the text mining findings. Methods: SD and methodology terms were extracted from 3,720 sleep-related journal articles in the PubMed database by using MetaMap. The extracted data set was analyzed using hierarchical cluster analyses and adjusted logistic regression models to investigate publication trends and associations between SD and methodology terms. Results: MetaMap had a text mining precision, recall, and false positive rate of 0.70, 0.77, and 11.51%, respectively. The most common SD term was breathing-related sleep disorder, whereas narcolepsy was the least common. Cluster analyses showed similar methodology clusters for each SD term, except narcolepsy. The logistic regression models showed an increasing prevalence of insomnia, parasomnia, and other sleep disorders but a decreasing prevalence of breathing-related sleep disorder during 2000–2013. Different SD terms were positively associated with different methodology terms regarding research design terms, measure terms, and analysis terms. Conclusion: Insomnia-, parasomnia-, and other sleep disorder-related articles showed an increasing publication trend, whereas those related to breathing-related sleep disorder showed a decreasing trend. Furthermore, experimental studies more commonly focused on hypersomnia and other SDs and less commonly on insomnia, breathing-related sleep disorder, narcolepsy, and parasomnia. Thus, text mining may facilitate the exploration of the publication trends in SDs and the associated methodologies.

Suggested Citation

  • Calvin Lam & Fu-Chih Lai & Chia-Hui Wang & Mei-Hsin Lai & Nanly Hsu & Min-Huey Chung, 2016. "Text Mining of Journal Articles for Sleep Disorder Terminologies," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-14, May.
  • Handle: RePEc:plo:pone00:0156031
    DOI: 10.1371/journal.pone.0156031
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    Cited by:

    1. Yuzhuo Wang & Chengzhi Zhang & Kai Li, 2022. "A review on method entities in the academic literature: extraction, evaluation, and application," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2479-2520, May.

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