A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Improving layman readability of clinical narratives with unsupervised synonym replacement
Tekijät: Moen H., Peltonen L., Koivumäki M., Suhonen H., Salakoski T., Ginter F., Salanterä S.
Toimittaja: Adrien Ugon, Daniel Karlsson, Gunnar O. Klein, Anne Moen
Konferenssin vakiintunut nimi: Conference on Medical Informatics Europe
Kustantaja: IOS Press
Julkaisuvuosi: 2018
Journal: Medical informatics Europe
Kokoomateoksen nimi: Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth
Tietokannassa oleva lehden nimi: Studies in Health Technology and Informatics
Sarjan nimi: Studies in Health Technology and Informatics
Vuosikerta: 247
Aloitussivu: 725
Lopetussivu: 729
Sivujen määrä: 5
ISBN: 978-1-61499-851-8
eISBN: 978-1-61499-852-5
ISSN: 0926-9630
DOI: https://doi.org/10.3233/978-1-61499-852-5-725
Verkko-osoite: http://ebooks.iospress.nl/publication/48887
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/32055606
We report on the development and evaluation of a prototype tool aimed to assist laymen/patients in understanding the content of clinical narratives. The tool relies largely on unsupervised machine learning applied to two large corpora of unlabeled text – a clinical corpus and a general domain corpus. A joint semantic word-space model is created for the purpose of extracting easier to understand alternatives for words considered difficult to understand by laymen. Two domain experts evaluate the tool and inter-rater agreement is calculated. When having the tool suggest ten alternatives to each difficult word, it suggests acceptable lay words for 55.51% of them. This and future manual evaluation will serve to further improve performance, where also supervised machine learning will be used.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
