Vertaisarvioitu artikkeli konferenssijulkaisussa (A4)
Text Classification Model Explainability for Keyword Extraction - Towards Keyword-Based Summarization of Nursing Care Episodes
Julkaisun tekijät: Reunamo Akseli, Peltonen Laura-Maria, Mustonen Reetta, Saari Minttu, Salakoski Tapio, Salanterä Sanna, Moen Hans
Toimittaja: Paula Otero, Philip Scott, Susan Z. Martin, Elaine Huesing
Konferenssin vakiintunut nimi: World Congress on Medical and Health Informatics
Julkaisuvuosi: 2022
Journal: Studies in Health Technology and Informatics
Kirjan nimi *: MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation
Tietokannassa oleva lehden nimi: Studies in health technology and informatics
Lehden akronyymi: Stud Health Technol Inform
Sarjan nimi: Studies in Health Technology and Informatics
Volyymi: 290
Aloitussivu: 632
Lopetussivun numero: 636
ISBN: 978-1-64368-264-8
eISBN: 978-1-64368-265-5
ISSN: 0926-9630
eISSN: 1879-8365
DOI: http://dx.doi.org/10.3233/SHTI220154
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/175561978
Tools to automate the summarization of nursing entries in electronic health records (EHR) have the potential to support healthcare professionals to obtain a rapid overview of a patient's situation when time is limited. This study explores a keyword-based text summarization method for the nursing text that is based on machine learning model explainability for text classification models. This study aims to extract keywords and phrases that provide an intuitive overview of the content in multiple nursing entries in EHRs written during individual patients' care episodes. The proposed keyword extraction method is used to generate keyword summaries from 40 patients' care episodes and its performance is compared to a baseline method based on word embeddings combined with the PageRank method. The two methods were assessed with manual evaluation by three domain experts. The results indicate that it is possible to generate representative keyword summaries from nursing entries in EHRs and our method outperformed the baseline method.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
