A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Clustering Nursing Sentences - Comparing Three Sentence Embedding Methods
Tekijät: Moen Hans, Suhonen Henry, Salanterä Sanna, Salakoski Tapio, Peltonen Laura-Maria
Toimittaja: Brigitte Séroussi, Patrick Weber, Ferdinand Dhombres, Cyril Grouin, Jan-David Liebe, Sylvia Pelayo, Andrea Pinna, Bastien Rance, Lucia Sacchi, Adrien Ugon, Arriel Benis, Parisis Gallos
Konferenssin vakiintunut nimi: Medical Informatics Europe
Julkaisuvuosi: 2022
Journal: Medical informatics Europe
Kokoomateoksen nimi: Challenges of Trustable AI and Added-Value on Health
Tietokannassa oleva lehden nimi: Studies in health technology and informatics
Lehden akronyymi: Stud Health Technol Inform
Sarjan nimi: Studies in Health Technology and Informatics
Vuosikerta: 294
Aloitussivu: 854
Lopetussivu: 858
ISBN: 978-1-64368-284-6
eISBN: 978-1-64368-285-3
ISSN: 0926-9630
eISSN: 1879-8365
DOI: https://doi.org/10.3233/SHTI220606
Verkko-osoite: https://ebooks.iospress.nl/doi/10.3233/SHTI220606
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/178641781
In health sciences, high-quality text embeddings may augment qualitative data analysis of large amounts of text by enabling, e.g., searching and clustering of health information. This study aimed to evaluate three different sentence-level embedding methods in clustering sentences in nursing narratives from individual patients' hospital care episodes. Two of these embeddings are generated from language models based on the BERT framework, and the third on the Sent2Vec method. These embedding methods were used to cluster sentences from 20 patient care episodes and the results were manually evaluated. Findings suggest that the best clusters were produced by the embeddings from a BERT model fine-tuned for the proxy task of predicting subject headings for nursing text.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
