Towards Automated Screening of Literature on Artificial Intelligence in Nursing

Hans Moen, Dari Alhuwail, Jari Björne, Lori Block, Sven Celin, Eunjoo Jeon, Karl Kreiner, James Mitchell, Gabriela Ožegović, Charlene Esteban Ronquillo, Lydia Sequeira, Jude Tayaben, Max Topaz, Sai Veeranki, Laura-Maria Peltonen

Publikation: Beitrag in Buch oder TagungsbandBeitrag in Tagungsband ohne Präsentation


We evaluate the performance of multiple text classification methods used to automate the screening of article abstracts in terms of their relevance to a topic of interest. The aim is to develop a system that can be first trained on a set of manually screened article abstracts before using it to identify additional articles on the same topic. Here the focus is on articles related to the topic “artificial intelligence in nursing”. Eight text classification methods are tested, as well as two simple ensemble systems. The results indicate that it is feasible to use text classification technology to support the manual screening process of article abstracts when conducting a literature review. The best results are achieved by an ensemble system, which achieves a F1-score of 0.41, with a sensitivity of 0.54 and a specificity of 0.96. Future work directions are discussed.
TitelMEDINFO 2021: One World, One Health–Global Partnership for Digital Innovation
Redakteure/-innenPaula Otero, Philip Scott, Susan Z. Martin, Elaine Huesing
Herausgeber (Verlag)IOS Press
ISBN (Print)978-1-64368-264-8
PublikationsstatusVeröffentlicht - 2022

Research Field

  • Exploration of Digital Health


  • Natural Language Processing; Nursing; Review


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