Refereed article in conference proceedings (A4)

Text Classification Model Explainability for Keyword Extraction - Towards Keyword-Based Summarization of Nursing Care Episodes

List of Authors: Reunamo Akseli, Peltonen Laura-Maria, Mustonen Reetta, Saari Minttu, Salakoski Tapio, Salanterä Sanna, Moen Hans

Conference name: World Congress on Medical and Health Informatics

Publication year: 2022

Journal: Studies in Health Technology and Informatics

Book title *: MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation

Journal name in source: Studies in health technology and informatics

Journal acronym: Stud Health Technol Inform

Title of series: Studies in Health Technology and Informatics

Volume number: 290

ISBN: 978-1-64368-264-8

eISBN: 978-1-64368-265-5

ISSN: 0926-9630

eISSN: 1879-8365


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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.

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Last updated on 2022-23-06 at 14:10