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Machine learning approaches applied in spinal pain research.

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Title: Machine learning approaches applied in spinal pain research.
Authors: Falla, Deborah1 d.falla@bham.ac.uk, Devecchi, Valter1, Jiménez-Grande, David1, Rügamer, David2, Liew, Bernard X.W.3
Source: Journal of Electromyography & Kinesiology. Dec2021, Vol. 61, pN.PAG-N.PAG. 1p.
Subjects: Machine learning, Lumbar pain, Patient management, Spinal cord, Physiology, Skeletal muscle, Pain
Abstract: The purpose of this narrative review is to provide a critical reflection of how analytical machine learning approaches could provide the platform to harness variability of patient presentation to enhance clinical prediction. The review includes a summary of current knowledge on the physiological adaptations present in people with spinal pain. We discuss how contemporary evidence highlights the importance of not relying on single features when characterizing patients given the variability of physiological adaptations present in people with spinal pain. The advantages and disadvantages of current analytical strategies in contemporary basic science and epidemiological research are reviewed and we consider how analytical machine learning approaches could provide the platform to harness the variability of patient presentations to enhance clinical prediction of pain persistence or recurrence. We propose that machine learning techniques can be leveraged to translate a potentially heterogeneous set of variables into clinically useful information with the potential to enhance patient management. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Electromyography & Kinesiology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Machine learning approaches applied in spinal pain research.
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Lumbar+pain%22">Lumbar pain</searchLink><br /><searchLink fieldCode="DE" term="%22Patient+management%22">Patient management</searchLink><br /><searchLink fieldCode="DE" term="%22Spinal+cord%22">Spinal cord</searchLink><br /><searchLink fieldCode="DE" term="%22Physiology%22">Physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Skeletal+muscle%22">Skeletal muscle</searchLink><br /><searchLink fieldCode="DE" term="%22Pain%22">Pain</searchLink>
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  Data: The purpose of this narrative review is to provide a critical reflection of how analytical machine learning approaches could provide the platform to harness variability of patient presentation to enhance clinical prediction. The review includes a summary of current knowledge on the physiological adaptations present in people with spinal pain. We discuss how contemporary evidence highlights the importance of not relying on single features when characterizing patients given the variability of physiological adaptations present in people with spinal pain. The advantages and disadvantages of current analytical strategies in contemporary basic science and epidemiological research are reviewed and we consider how analytical machine learning approaches could provide the platform to harness the variability of patient presentations to enhance clinical prediction of pain persistence or recurrence. We propose that machine learning techniques can be leveraged to translate a potentially heterogeneous set of variables into clinically useful information with the potential to enhance patient management. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Electromyography & Kinesiology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=153784956
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      – Type: doi
        Value: 10.1016/j.jelekin.2021.102599
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Lumbar pain
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      – SubjectFull: Patient management
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      – SubjectFull: Spinal cord
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      – SubjectFull: Skeletal muscle
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      – SubjectFull: Pain
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            NameFull: Rügamer, David
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              Text: Dec2021
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