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Mathematical and Computational Models for Pain: A Systematic Review.

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Mathematical and computational models for pain are understudied. This review found most models classify pain, but future research should focus on mechanisms for better diagnostics and treatment.

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Area of Science:

  • Computational neuroscience
  • Pain research
  • Mathematical modeling

Background:

  • Current pain theories are incomplete, lacking a unified explanation for pain's origin, qualities, and alleviation.
  • While molecular targets are studied, few computational or mathematical models explore pain's underlying mechanisms or etiology.

Purpose of the Study:

  • To systematically identify and classify existing mathematical and computational models used for characterizing pain.
  • To assess the current state and future potential of computational approaches in pain research.

Main Methods:

  • A systematic literature search was conducted on Science Direct and PubMed.
  • Searched articles published before January 1st, 2020.
  • Included articles that incorporated mathematical or computational models of pain, resulting in 31 relevant studies.

Main Results:

  • The majority of reviewed studies employed classification algorithms to distinguish between pain and no-pain states.
  • The literature predominantly focuses on applying existing models or machine learning for pain detection, rather than exploring pain's diagnostic or therapeutic features.

Conclusions:

  • Mathematical models offer a promising avenue to enhance pain understanding by guiding testable hypotheses on pain mechanisms.
  • Further development of models focused on elucidating pain's underlying mechanisms is crucial for potential treatment breakthroughs.