Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/24766
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dc.contributor.authorMadanu, R-
dc.contributor.authorAbbod, MF-
dc.contributor.authorHsiao, F-J-
dc.contributor.authorChen, W-T-
dc.contributor.authorShieh, J-S-
dc.date.accessioned2022-07-01T13:32:15Z-
dc.date.available2022-07-01T13:32:15Z-
dc.date.issued2020-06-14-
dc.identifier.citationMadanu. R., Abbod. M.F., Hsiao. F-J., Chen. W-T., and Shieh. J-S. (2022) 'Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review' Technologies 10, 3, pp. 1 - 15. doi:org/10.3390/technologies10030074.en_US
dc.identifier.issn2227-7080-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/24766-
dc.description.abstractPain is a complex term that describes various sensations that create discomfort in various ways or types inside the human body. Generally, pain has consequences that range from mild to severe in different organs of the body and will depend on the way it is caused, which could be an injury, illness or medical procedures including testing, surgeries or therapies, etc. With recent advances in artificial-intelligence (AI) systems associated in biomedical and healthcare settings, the contiguity of physician, clinician and patient has shortened. AI, however, has more scope to interpret the pain associated in patients with various conditions by using any physiological or behavioral changes. Facial expressions are considered to give much information that relates with emotions and pain, so clinicians consider these changes with high importance for assessing pain. This has been achieved in recent times with different machine-learning and deep-learning models. To accentuate the future scope and importance of AI in medical field, this study reviews the explainable AI (XAI) as increased attention is given to an automatic assessment of pain. This review discusses how these approaches are applied for different pain types.en_US
dc.description.sponsorshipThis research was funded by Ministry of Science and Technology (MOST) of Taiwan, grant number: MOST 110-2221-E-155-004-MY2.en_US
dc.format.extent1 - 15-
dc.format.mediumPrint - Electronic-
dc.languageen-
dc.language.isoen_USen_US
dc.publisherMDPI AGen_US
dc.rightsCopyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectpainen_US
dc.subjecthealthcareen_US
dc.subjectneural networksen_US
dc.subjectartificial intelligenceen_US
dc.subjectexplainable AIen_US
dc.titleExplainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Reviewen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.3390/technologies10030074-
dc.relation.isPartOfTechnologies-
pubs.issue3-
pubs.publication-statusPublished online-
pubs.volume10-
dc.identifier.eissn2227-7080-
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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