Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/24766
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Madanu, R | - |
dc.contributor.author | Abbod, MF | - |
dc.contributor.author | Hsiao, F-J | - |
dc.contributor.author | Chen, W-T | - |
dc.contributor.author | Shieh, J-S | - |
dc.date.accessioned | 2022-07-01T13:32:15Z | - |
dc.date.available | 2022-07-01T13:32:15Z | - |
dc.date.issued | 2020-06-14 | - |
dc.identifier | ORCID iD: Maysam F..Abbod https://orcid.org/0000-0002-8515-7933 | - |
dc.identifier | ORCID iD: Fu-JungHsiao https://orcid.org/0000-0001-7824-8864 | - |
dc.identifier | 74 | - |
dc.identifier.citation | Madanu. R. et al. (2022) 'Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review', Technologies, 10 (3), 74, pp. 1 - 15. doi: 10.3390/technologies10030074. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/24766 | - |
dc.description.abstract | Copyright © 2022 by the authors. Pain 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.sponsorship | This 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.extent | 1 - 15 | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | Copyright © 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.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | pain | en_US |
dc.subject | healthcare | en_US |
dc.subject | neural networks | en_US |
dc.subject | artificial intelligence | en_US |
dc.subject | explainable AI | en_US |
dc.title | Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/technologies10030074 | - |
dc.relation.isPartOf | Technologies | - |
pubs.issue | 3 | - |
pubs.publication-status | Published online | - |
pubs.volume | 10 | - |
dc.identifier.eissn | 2227-7080 | - |
dc.rights.holder | The authors | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | Copyright © 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/). | 766.37 kB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License