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Machine learning approaches applied in spinal pain research.

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Bibliographic Details
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]
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Database: Engineering Source
Description
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]
ISSN:10506411
DOI:10.1016/j.jelekin.2021.102599