Towards automatic and interpretable assignments of patients presenting with pain to the emergency department
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SHTI-276-SHTI210005. Available under License Creative Commons Attribution Non-commercial 4.0. |
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Description
Introduction: Pain is the most common symptom that patients present with to the emergency department. It is hard to identify patients who have presented in pain to the emergency department when compliance with structured pain assessment is low. An ability to identify patients presenting in pain allows further investigation of the quality of care provided. Background: Machine and deep learning techniques are commonly used for text analysis in healthcare. Applications such as the classification of diagnosis and unplanned readmissions from textual medical records have previously been described. In other work, conventional and deep-learning techniques have demonstrated high performance in identifying patients presenting to the emergency department in pain. However, these models have lacked interpretability. Methods: This paper proposes the use of machine learning techniques to identify patients who present in pain based upon their initial assessment using interpretable deep learning models. Results: The interpretable deep learning model of pain identification was shown to have more accuracy and precision than other machine and deep learning techniques. This technique has significant application to large datasets for the identification of the quality of care and real-Time identification of patients presenting in pain to improve their care.
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ID Code: | 212639 | ||
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Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||
Series Name: | Studies in Health Technology and Informatics | ||
ORCID iD: |
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Measurements or Duration: | 6 pages | ||
Keywords: | deep learning, Emergency Department, machine learning, nursing assessment, pain | ||
DOI: | 10.3233/SHTI210005 | ||
ISBN: | 978-1-64368-168-9 | ||
Pure ID: | 96884618 | ||
Divisions: | Current > Research Centres > Centre for Healthcare Transformation Current > QUT Faculties and Divisions > Faculty of Health Current > Schools > School of Nursing |
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Copyright Owner: | © 2021 The authors and IOS Press. | ||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||
Deposited On: | 18 Aug 2021 02:48 | ||
Last Modified: | 18 Apr 2024 10:53 |
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