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Text Mining of Journal Articles for Sleep Disorder Terminologies

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–...

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Autores principales: Lam, Calvin, Lai, Fu-Chih, Wang, Chia-Hui, Lai, Mei-Hsin, Hsu, Nanly, Chung, Min-Huey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874549/
https://www.ncbi.nlm.nih.gov/pubmed/27203858
http://dx.doi.org/10.1371/journal.pone.0156031
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author Lam, Calvin
Lai, Fu-Chih
Wang, Chia-Hui
Lai, Mei-Hsin
Hsu, Nanly
Chung, Min-Huey
author_facet Lam, Calvin
Lai, Fu-Chih
Wang, Chia-Hui
Lai, Mei-Hsin
Hsu, Nanly
Chung, Min-Huey
author_sort Lam, Calvin
collection PubMed
description 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.
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spelling pubmed-48745492016-06-09 Text Mining of Journal Articles for Sleep Disorder Terminologies Lam, Calvin Lai, Fu-Chih Wang, Chia-Hui Lai, Mei-Hsin Hsu, Nanly Chung, Min-Huey PLoS One Research Article 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. Public Library of Science 2016-05-20 /pmc/articles/PMC4874549/ /pubmed/27203858 http://dx.doi.org/10.1371/journal.pone.0156031 Text en © 2016 Lam et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lam, Calvin
Lai, Fu-Chih
Wang, Chia-Hui
Lai, Mei-Hsin
Hsu, Nanly
Chung, Min-Huey
Text Mining of Journal Articles for Sleep Disorder Terminologies
title Text Mining of Journal Articles for Sleep Disorder Terminologies
title_full Text Mining of Journal Articles for Sleep Disorder Terminologies
title_fullStr Text Mining of Journal Articles for Sleep Disorder Terminologies
title_full_unstemmed Text Mining of Journal Articles for Sleep Disorder Terminologies
title_short Text Mining of Journal Articles for Sleep Disorder Terminologies
title_sort text mining of journal articles for sleep disorder terminologies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874549/
https://www.ncbi.nlm.nih.gov/pubmed/27203858
http://dx.doi.org/10.1371/journal.pone.0156031
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