Towards ML-supported Triage Prediction in Real-World Emergency Room Scenarios

Faraz Maschhur, Klaus Netter, Sven Schmeier, Katrin Ostermann, Rimantas Palunis, Tobias Strapatsas, Roland Roller


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.
Anthology ID:
2024.bionlp-1.46
Volume:
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Dina Demner-Fushman, Sophia Ananiadou, Makoto Miwa, Kirk Roberts, Junichi Tsujii
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
559–569
Language:
URL:
https://aclanthology.org/2024.bionlp-1.46
DOI:
10.18653/v1/2024.bionlp-1.46
Bibkey:
Cite (ACL):
Faraz Maschhur, Klaus Netter, Sven Schmeier, Katrin Ostermann, Rimantas Palunis, Tobias Strapatsas, and Roland Roller. 2024. Towards ML-supported Triage Prediction in Real-World Emergency Room Scenarios. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 559–569, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Towards ML-supported Triage Prediction in Real-World Emergency Room Scenarios (Maschhur et al., BioNLP-WS 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.bionlp-1.46.pdf