Machine learning approaches applied in spinal pain research.

Area of Science:

• Biomedical informatics
• Clinical prediction modeling
• Pain research

Background:

• Spinal pain is characterized by significant variability in physiological adaptations.
• Current analytical strategies often rely on single features, overlooking patient heterogeneity.
• Understanding this variability is crucial for accurate clinical prediction.

Purpose of the Study:

• To critically review the application of analytical machine learning (ML) in harnessing patient presentation variability for enhanced clinical prediction.
• To explore how ML can translate heterogeneous variables into clinically useful information for spinal pain management.
• To summarize current knowledge on physiological adaptations in spinal pain and evaluate existing analytical methods.

Main Methods:

• Narrative review of existing literature on analytical strategies and machine learning in spinal pain research.
• Discussion of physiological adaptations and their variability in patients with spinal pain.
• Critical evaluation of the advantages and disadvantages of current analytical approaches.

Main Results:

• Contemporary evidence emphasizes the need to move beyond single-feature analysis due to physiological variability in spinal pain.
• Analytical machine learning offers a platform to integrate and interpret complex, heterogeneous patient data.
• ML techniques can potentially enhance the prediction of pain persistence or recurrence.

Conclusions:

• Machine learning approaches are well-suited to address the complexity and variability inherent in spinal pain presentations.
• Leveraging ML can translate diverse patient data into actionable insights for improved clinical decision-making and patient management.
• This review highlights the potential of ML to advance the field of clinical prediction for spinal pain.