Clustering Nursing Sentences - Comparing Three Sentence Embedding Methods
: Moen Hans, Suhonen Henry, Salanterä Sanna, Salakoski Tapio, Peltonen Laura-Maria
: 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
: Medical Informatics Europe
: 2022
: Medical informatics Europe
: Challenges of Trustable AI and Added-Value on Health
: Studies in health technology and informatics
: Stud Health Technol Inform
: Studies in Health Technology and Informatics
: 294
: 854
: 858
: 978-1-64368-284-6
: 978-1-64368-285-3
: 0926-9630
: 1879-8365
DOI: https://doi.org/10.3233/SHTI220606
: https://ebooks.iospress.nl/doi/10.3233/SHTI220606
: 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.