{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T07:52:53Z","timestamp":1768031573764,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643682846","type":"print"},{"value":"9781643682853","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:00:00Z","timestamp":1653436800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,25]]},"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.3233\/shti220606","type":"book-chapter","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:17:53Z","timestamp":1653481073000},"source":"Crossref","is-referenced-by-count":2,"title":["Clustering Nursing Sentences \u2013 Comparing Three Sentence Embedding Methods"],"prefix":"10.3233","author":[{"given":"Hans","family":"Moen","sequence":"first","affiliation":[{"name":"Department of Computer Science, Aalto University, Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henry","family":"Suhonen","sequence":"additional","affiliation":[{"name":"Department of Nursing Science, University of Turku, Turku, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanna","family":"Salanter\u00e4","sequence":"additional","affiliation":[{"name":"Department of Nursing Science, University of Turku, Turku, Finland"},{"name":"Turku University Hospital, Turku, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tapio","family":"Salakoski","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, University of Turku, Turku, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura-Maria","family":"Peltonen","sequence":"additional","affiliation":[{"name":"Department of Nursing Science, University of Turku, Turku, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Challenges of Trustable AI and Added-Value on Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220606","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:17:53Z","timestamp":1653481073000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220606"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,25]]},"ISBN":["9781643682846","9781643682853"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220606","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,25]]}}}