Work

Delirium Identification from Nursing Reports Using Large Language Models

Year: 2025

Type: book-chapter

Abstract: 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.

Source: Studies in health technology and informatics

DOI: 10.3233/shti250492

Authors: Lisa Graf, Alexander Ritzi, Lili M. Schoeler

Institutions: University of Freiburg, University Medical Center Freiburg, University of Turku

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4410447643

FWCI: 8.409

Cites:

Cited by: 4

Related to: 10