@inproceedings{maschhur-etal-2024-towards,
title = "Towards {ML}-supported Triage Prediction in Real-World Emergency Room Scenarios",
author = "Maschhur, Faraz and
Netter, Klaus and
Schmeier, Sven and
Ostermann, Katrin and
Palunis, Rimantas and
Strapatsas, Tobias and
Roller, Roland",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bionlp-1.46",
pages = "559--569",
abstract = "In emergency wards, patients are prioritized by clinical staff according to the urgency of their medical condition. This can be achieved by categorizing patients into different labels of urgency ranging from immediate to not urgent. However, in order to train machine learning models offering support in this regard, there is more than approaching this as a multi-class problem. This work explores the challenges and obstacles of automatic triage using anonymized real-world multi-modal ambulance data in Germany.",
}
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%0 Conference Proceedings
%T Towards ML-supported Triage Prediction in Real-World Emergency Room Scenarios
%A Maschhur, Faraz
%A Netter, Klaus
%A Schmeier, Sven
%A Ostermann, Katrin
%A Palunis, Rimantas
%A Strapatsas, Tobias
%A Roller, Roland
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F maschhur-etal-2024-towards
%X In emergency wards, patients are prioritized by clinical staff according to the urgency of their medical condition. This can be achieved by categorizing patients into different labels of urgency ranging from immediate to not urgent. However, in order to train machine learning models offering support in this regard, there is more than approaching this as a multi-class problem. This work explores the challenges and obstacles of automatic triage using anonymized real-world multi-modal ambulance data in Germany.
%U https://aclanthology.org/2024.bionlp-1.46
%P 559-569
Markdown (Informal)
[Towards ML-supported Triage Prediction in Real-World Emergency Room Scenarios](https://aclanthology.org/2024.bionlp-1.46) (Maschhur et al., BioNLP-WS 2024)
ACL