A4 Refereed article in a conference publication
Delirium Identification from Nursing Reports Using Large Language Models
Authors: Graf, Lisa; Ritzi, Alexander; Schöler, Lili M.
Editors: Andrikopoulou, Elisavet; Gallos, Parisis; Arvanitis, Theodoros N.; Austin, Rosalynn; Benis, Arriel; Cornet, Ronald; Chatzistergos, Panagiotis; Dejaco, Alexander; Dusseljee-Peute, Linda; Mohasseb, Alaa; Natsiavas, Pantelis; Nakkas, Haythem; Scott, Philip
Conference name: Medical Informatics Europe Conference
Publisher: IOS Press
Publication year: 2025
Journal: Studies in Health Technology and Informatics
Book title : Intelligent Health Systems – From Technology to Data and Knowledge: Proceedings of MIE 2025
Volume: 327
First page : 886
Last page: 887
eISBN: 978-1-64368-596-0
ISSN: 0926-9630
eISSN: 1879-8365
DOI: https://doi.org/10.3233/SHTI250492
Web address : https://doi.org/10.3233/shti250492
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/499069125
This study investigates large language models for delirium detection from nursing reports, comparing keyword matching, prompting, and finetuning. Using a manually labelled dataset from the University Hospital Freiburg, Germany, we tested Llama3 and Phi3 models. Both prompting and finetuning were effective, with finetuning Phi3 (3.8B) achieving the highest accuracy (90.24%) and AUROC (96.07%), significantly outperforming other methods.
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