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MESH classification...
MESH classification of clinical guidelinesusing conceptual embeddings of references
- Article/chapterEnglish2019
Publisher, publication year, extent ...
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2019
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electronicrdacarrier
Numbers
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LIBRIS-ID:oai:DiVA.org:hb-22096
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https://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-22096URI
Supplementary language notes
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Language:English
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Summary in:English
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Classification
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Subject category:ref swepub-contenttype
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Subject category:kon swepub-publicationtype
Notes
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In this study, we investigate different strategies for assigning MeSH (Medical Subject Headings) terms to clinical guidelines using machine learning. Features based on words in titles and abstracts are investigated and compared to features based on topics assigned to references cited by the guidelines. Two of the feature engineering strategies utilize word embeddings produced by recent models based on in the distributional hypothesis, called word2vecand fastText. The evaluation results show that reference-based strategies tend to yield a higher recall and F1 scores for MeSH terms with a sufficient amount of training instances, whereas title and abstract based features yield a higher precision.
Subject headings and genre
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Gunnarsson Lorenzen, David,1981-Högskolan i Borås,Akademin för bibliotek, information, pedagogik och IT(Swepub:hb)dgu
(author)
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Nelhans, Gustaf,1973-Högskolan i Borås,Akademin för bibliotek, information, pedagogik och IT(Swepub:hb)GUNE
(author)
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Högskolan i BoråsAkademin för bibliotek, information, pedagogik och IT
(creator_code:org_t)
Related titles
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In:Proceedings of the 17th conference of the International society for scientometrics and informetrics, ISSI, s. 859-8649788833811185
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