Primer on machine learning: utilization of large data set analyses to individualize pain management

Credit: ncbi.nlm.nih.gov / Curr Opin Anaesthesiol. 2020

Pain researchers and clinicians increasingly encounter machine learning algorithms in both research methods and clinical practice. This review provides a summary of key machine learning principles, as well as applications to both structured and unstructured datasets.

Aside from increasing use in the analysis of electronic health record data, machine and deep learning algorithms are now key tools in the analyses of neuroimaging and facial expression recognition data used in pain research.

Summary

In the coming years, machine learning is likely to become a key component of evidence-based medicine, yet will require additional skills and perspectives for its successful and ethical use in research and clinical settings.

In certain circumstances and contexts, machine learning may offer advantages over traditional techniques.
One of the key opportunities for machine learning in pain research and clinical practice pertains to the use of complex and unstructured data.
Patients, physicians, and researchers will need to address important ethical issues as machine learning methods and applications to pain continue to evolve.

Article source:
Rashidi P, Edwards DA, Tighe PJ. Primer on machine learning: utilization of large data set analyses to individualize pain management. Curr Opin Anaesthesiol. 2019;32(5):653-660. doi:10.1097/ACO.0000000000000779