Machine learning approaches applied in spinal pain research

Deborah Falla, Valter Devecchi, David Jiménez-Grande, David Rügamer, Bernard X W Liew

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number102599
JournalJournal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
Volume61
Early online date17 Sep 2021
DOIs
Publication statusPublished - Dec 2021

Bibliographical note

Copyright © 2021 Elsevier Ltd. All rights reserved.

Keywords

  • Low back pain
  • Neck pain
  • Machine learning
  • Classification
  • Prediction
  • Modelling

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