dc.contributor.author | Jenssen, Marit Dagny Kristine | |
dc.contributor.author | Bakkevoll, Per Atle | |
dc.contributor.author | Ngo, Phuong | |
dc.contributor.author | Budrionis, Andrius | |
dc.contributor.author | Fagerlund, Asbjørn Johansen | |
dc.contributor.author | Tayefi, Maryam | |
dc.contributor.author | Bellika, Johan Gustav | |
dc.contributor.author | Godtliebsen, Fred | |
dc.date.accessioned | 2021-06-30T11:40:14Z | |
dc.date.available | 2021-06-30T11:40:14Z | |
dc.date.issued | 2021-04-02 | |
dc.description.abstract | Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care. | en_US |
dc.identifier.citation | Jenssen MDK, Bakkevoll Pa, Ngo P, Budrionis A, Fagerlund AJ, Tayefi M, Bellika JG, Godtliebsen F. Machine Learning in Chronic Pain Research: A Scoping Review. Applied Sciences. 2021;11(7) | en_US |
dc.identifier.cristinID | FRIDAID 1907727 | |
dc.identifier.doi | 10.3390/app11073205 | |
dc.identifier.issn | 2076-3417 | |
dc.identifier.uri | https://hdl.handle.net/10037/21638 | |
dc.language.iso | eng | en_US |
dc.publisher | MDPI | en_US |
dc.relation.journal | Applied Sciences | |
dc.rights.accessRights | openAccess | en_US |
dc.rights.holder | Copyright 2021 The Author(s) | en_US |
dc.subject | VDP::Mathematics and natural science: 400 | en_US |
dc.subject | VDP::Matematikk og Naturvitenskap: 400 | en_US |
dc.title | Machine Learning in Chronic Pain Research: A Scoping Review | en_US |
dc.type.version | publishedVersion | en_US |
dc.type | Journal article | en_US |
dc.type | Tidsskriftartikkel | en_US |
dc.type | Peer reviewed | en_US |