Cargando…
Mathematical and Computational Models for Pain: A Systematic Review
OBJECTIVE: 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 comput...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665994/ https://www.ncbi.nlm.nih.gov/pubmed/34051102 http://dx.doi.org/10.1093/pm/pnab177 |
_version_ | 1784614120502329344 |
---|---|
author | Lang, Victoria Ashley Lundh, Torbjörn Ortiz-Catalan, Max |
author_facet | Lang, Victoria Ashley Lundh, Torbjörn Ortiz-Catalan, Max |
author_sort | Lang, Victoria Ashley |
collection | PubMed |
description | OBJECTIVE: 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 model development. In this systematic review, we identified and classified mathematical and computational models for characterizing pain. METHODS: 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. RESULTS: 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. CONCLUSIONS: Although understudied, the development of mathematical models may augment the current understanding of pain by providing directions for testable hypotheses of its underlying mechanisms. Additional focus is needed on developing models that seek to understand the underlying mechanisms of pain, as this could potentially lead to major breakthroughs in its treatment. |
format | Online Article Text |
id | pubmed-8665994 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-86659942021-12-13 Mathematical and Computational Models for Pain: A Systematic Review Lang, Victoria Ashley Lundh, Torbjörn Ortiz-Catalan, Max Pain Med Acute, Regional Anesthesiology & Perioperative Pain Section OBJECTIVE: 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 model development. In this systematic review, we identified and classified mathematical and computational models for characterizing pain. METHODS: 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. RESULTS: 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. CONCLUSIONS: Although understudied, the development of mathematical models may augment the current understanding of pain by providing directions for testable hypotheses of its underlying mechanisms. Additional focus is needed on developing models that seek to understand the underlying mechanisms of pain, as this could potentially lead to major breakthroughs in its treatment. Oxford University Press 2021-05-29 /pmc/articles/PMC8665994/ /pubmed/34051102 http://dx.doi.org/10.1093/pm/pnab177 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the American Academy of Pain Medicine. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Acute, Regional Anesthesiology & Perioperative Pain Section Lang, Victoria Ashley Lundh, Torbjörn Ortiz-Catalan, Max Mathematical and Computational Models for Pain: A Systematic Review |
title | Mathematical and Computational Models for Pain: A Systematic Review |
title_full | Mathematical and Computational Models for Pain: A Systematic Review |
title_fullStr | Mathematical and Computational Models for Pain: A Systematic Review |
title_full_unstemmed | Mathematical and Computational Models for Pain: A Systematic Review |
title_short | Mathematical and Computational Models for Pain: A Systematic Review |
title_sort | mathematical and computational models for pain: a systematic review |
topic | Acute, Regional Anesthesiology & Perioperative Pain Section |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665994/ https://www.ncbi.nlm.nih.gov/pubmed/34051102 http://dx.doi.org/10.1093/pm/pnab177 |
work_keys_str_mv | AT langvictoriaashley mathematicalandcomputationalmodelsforpainasystematicreview AT lundhtorbjorn mathematicalandcomputationalmodelsforpainasystematicreview AT ortizcatalanmax mathematicalandcomputationalmodelsforpainasystematicreview |