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 2000-2...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4874549?pdf=render |
| _version_ | 1818613691611873280 |
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| author | Calvin Lam Fu-Chih Lai Chia-Hui Wang Mei-Hsin Lai Nanly Hsu Min-Huey Chung |
| author_facet | Calvin Lam Fu-Chih Lai Chia-Hui Wang Mei-Hsin Lai Nanly Hsu Min-Huey Chung |
| author_sort | Calvin Lam |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-16T16:06:09Z |
| format | Article |
| id | doaj.art-0571976a484f434b979b43866e7046c8 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-16T16:06:09Z |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-0571976a484f434b979b43866e7046c82022-12-21T22:25:21ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01115e015603110.1371/journal.pone.0156031Text Mining of Journal Articles for Sleep Disorder Terminologies.Calvin LamFu-Chih LaiChia-Hui WangMei-Hsin LaiNanly HsuMin-Huey ChungOBJECTIVE: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.http://europepmc.org/articles/PMC4874549?pdf=render |
| spellingShingle | Calvin Lam Fu-Chih Lai Chia-Hui Wang Mei-Hsin Lai Nanly Hsu Min-Huey Chung Text Mining of Journal Articles for Sleep Disorder Terminologies. PLoS ONE |
| 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 |
| url | http://europepmc.org/articles/PMC4874549?pdf=render |
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