@inproceedings{vaaben-bornerup-hardmeier-2025-efficient,
title = "Efficient Elicitation of Fictitious Nursing Notes from Volunteer Healthcare Professionals",
author = "Vaaben Bornerup, Jesper and
Hardmeier, Christian",
editor = "Johansson, Richard and
Stymne, Sara",
booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
month = mar,
year = "2025",
address = "Tallinn, Estonia",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2025.nodalida-1.74/",
pages = "739--754",
ISBN = "978-9908-53-109-0",
abstract = "Reliable automatic solutions to extract structured information from free-text nursing notes could bring important efficiency gains in healthcare, but their development is hampered by the sensitivity and limited availability of example data. We describe a method for eliciting fictitious nursing documentation and associated structured documentation from volunteers and a resulting dataset of 397 Danish notes collected and annotated through a custom web application from 98 participating nurses. After some manual refinement, we obtained a high-quality dataset containing nurse notes with relevant entities identified. We describe the implementation and limitations of our approach as well as initial experiments in a named entity tagging setup."
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%0 Conference Proceedings
%T Efficient Elicitation of Fictitious Nursing Notes from Volunteer Healthcare Professionals
%A Vaaben Bornerup, Jesper
%A Hardmeier, Christian
%Y Johansson, Richard
%Y Stymne, Sara
%S Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)
%D 2025
%8 March
%I University of Tartu Library
%C Tallinn, Estonia
%@ 978-9908-53-109-0
%F vaaben-bornerup-hardmeier-2025-efficient
%X Reliable automatic solutions to extract structured information from free-text nursing notes could bring important efficiency gains in healthcare, but their development is hampered by the sensitivity and limited availability of example data. We describe a method for eliciting fictitious nursing documentation and associated structured documentation from volunteers and a resulting dataset of 397 Danish notes collected and annotated through a custom web application from 98 participating nurses. After some manual refinement, we obtained a high-quality dataset containing nurse notes with relevant entities identified. We describe the implementation and limitations of our approach as well as initial experiments in a named entity tagging setup.
%U https://aclanthology.org/2025.nodalida-1.74/
%P 739-754
Markdown (Informal)
[Efficient Elicitation of Fictitious Nursing Notes from Volunteer Healthcare Professionals](https://aclanthology.org/2025.nodalida-1.74/) (Vaaben Bornerup & Hardmeier, NoDaLiDa 2025)
ACL