  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.
