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Pain and Therapy
Article . 2021 . Peer-reviewed
License: CC BY NC
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Pain and Therapy
Article
License: CC BY NC
Data sources: UnpayWall
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Pain and Therapy
Review . 2021
License: CC BY NC
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Machine Learning in Pain Medicine: An Up-To-Date Systematic Review

An Up-To-Date Systematic Review
Authors: Maria Matsangidou; Andreas Liampas; Melpo Pittara; Constantinos S. Pattichi; Panagiotis Zis;

Machine Learning in Pain Medicine: An Up-To-Date Systematic Review

Abstract

Pain is the unpleasant sensation and emotional experience that leads to poor quality of life for millions of people worldwide. Considering the complexity in understanding the principles of pain and its significant impact on individuals and society, research focuses to deliver innovative pain relief methods and techniques. This review explores the clinical uses of machine learning (ML) for the diagnosis, classification, and management of pain.A systematic review of the current literature was conducted using the PubMed database library.Twenty-six papers related to pain and ML research were included. Most of the studies used ML for effectively classifying the patients' level of pain, followed by use of ML for the prediction of manifestation of pain and for pain management. A less common reason for performing ML analysis was for the diagnosis of pain. The different approaches are thoroughly discussed.ML is increasingly used in pain medicine and appears to be more effective compared to traditional statistical approaches in the diagnosis, classification, and management of pain.

Country
Netherlands
Keywords

Pain classification, Machine learning, Pain, Review, Pain manifestation, Unsupervised learning, Algorithms, Pain diagnosis, Pain management, Supervised learning

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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