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        <title>Identifying Patients with Pain in Emergency Departments using Conventional Machine Learning and Deep Learning</title>
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        <namePart type="given">Thanh</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Identifying Patients with Pain in Emergency Departments using Conventional Machine Learning and Deep Learning
%A Vu, Thanh
%A Nguyen, Anthony
%A Brown, Nathan
%A Hughes, James
%Y Mistica, Meladel
%Y Piccardi, Massimo
%Y MacKinlay, Andrew
%S Proceedings of the 17th Annual Workshop of the Australasian Language Technology Association
%D 2019
%8 4–6 dec
%I Australasian Language Technology Association
%C Sydney, Australia
%F vu-etal-2019-identifying
%X 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.
%U https://aclanthology.org/U19-1015
%P 111-119

Markdown (Informal)

[Identifying Patients with Pain in Emergency Departments using Conventional Machine Learning and Deep Learning](https://aclanthology.org/U19-1015) (Vu et al., ALTA 2019)

• Identifying Patients with Pain in Emergency Departments using Conventional Machine Learning and Deep Learning (Vu et al., ALTA 2019)

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.