Identifying Patients with Pain in Emergency Departments using Conventional Machine Learning and Deep Learning

Thanh Vu, Anthony Nguyen, Nathan Brown, James Hughes


Abstract
Pain is the main symptom that patients present with to the emergency department (ED). Pain management, however, is often poorly done aspect of emergency care and patients with painful conditions can endure long waits before their pain is assessed or treated. To improve pain management quality, identifying whether or not an ED patient presents with pain is an important task and allows for further investigation of the quality of care provided. In this paper, machine learning was utilised to handle the task of automatically detecting patients who present at EDs with pain from retrospective data. Experimental results on a manually annotated dataset show that our proposed machine learning models achieve high performances, in which the highest accuracy and macro-averaged F1 are 91.00% and 90.96%, respectively.
Anthology ID:
U19-1015
Volume:
Proceedings of the 17th Annual Workshop of the Australasian Language Technology Association
Month:
4--6 December
Year:
2019
Address:
Sydney, Australia
Editors:
Meladel Mistica, Massimo Piccardi, Andrew MacKinlay
Venue:
ALTA
SIG:
Publisher:
Australasian Language Technology Association
Note:
Pages:
111–119
Language:
URL:
https://aclanthology.org/U19-1015
DOI:
Bibkey:
Cite (ACL):
Thanh Vu, Anthony Nguyen, Nathan Brown, and James Hughes. 2019. Identifying Patients with Pain in Emergency Departments using Conventional Machine Learning and Deep Learning. In Proceedings of the 17th Annual Workshop of the Australasian Language Technology Association, pages 111–119, Sydney, Australia. Australasian Language Technology Association.
Cite (Informal):
Identifying Patients with Pain in Emergency Departments using Conventional Machine Learning and Deep Learning (Vu et al., ALTA 2019)
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PDF:
https://aclanthology.org/U19-1015.pdf