MESH classification of clinical guidelinesusing conceptual embeddings of references

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.

SAMHÄLLSVETENSKAP Medie- och kommunikationsvetenskap Biblioteks- och informationsvetenskap hsv//swe
SOCIAL SCIENCES Media and Communications Information Studies hsv//eng
MESH
clinical guidelines
machine learning
word embedding
word2vec
fasttext
Library and Information Science
Biblioteks- och informationsvetenskap

Gunnarsson Lorenzen, David,1981-Högskolan i Borås,Akademin för bibliotek, information, pedagogik och IT(Swepub:hb)dgu (author)
Nelhans, Gustaf,1973-Högskolan i Borås,Akademin för bibliotek, information, pedagogik och IT(Swepub:hb)GUNE (author)
Högskolan i BoråsAkademin för bibliotek, information, pedagogik och IT (creator_code:org_t)

In:Proceedings of the 17th conference of the International society for scientometrics and informetrics, ISSI, s. 859-8649788833811185

By the author/editor: Eklund, Johan; Gunnarsson Loren ...; Nelhans, Gustaf, ...

About the subject

SOCIAL SCIENCES: SOCIAL SCIENCES; and Media and Commun ...; and Information Stud ...

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