Mathematical models for pain: a systematic review

TL;DR

This systematic review examines mathematical and computational models of pain, highlighting their focus on classification and the need for models that explore pain mechanisms for better diagnostics and treatments.

Contribution

It provides a comprehensive overview of existing mathematical approaches to pain, emphasizing the gap in models that elucidate pain etiology and mechanisms.

Findings

Most studies used classification algorithms for pain detection

Limited research on models exploring pain mechanisms

Mathematical models could improve understanding and treatment of pain

Abstract

There is no single prevailing theory of pain that explains its origin, qualities, and alleviation. Although many studies have investigated various molecular targets for pain management, few have attempted to examine the etiology or working mechanisms of pain through mathematical or computational techniques. In this systematic review, we identified mathematical and computational approaches for characterizing pain. The databases queried were Science Direct and PubMed, yielding 560 articles published prior to January 1st, 2020. After screening for inclusion of mathematical or computational models of pain, 31 articles were deemed relevant. Most of the reviewed articles utilized classification algorithms to categorize pain and no-pain conditions. We found the literature heavily focused on the application of existing models or machine learning algorithms to identify the presence or absence of pain, rather than to explore features of pain that may be used for diagnostics and treatment. Although understudied, the development of mathematical models may augment the current understanding of pain by providing directions for testable hypotheses of its underlying mechanisms.