# Mathematical models for pain: a systematic review

**Authors:** Victoria Ashley Lang, Torbjörn Lundh, Max Ortiz-Catalan  
**arXiv:** [2006.01745](https://arxiv.org/abs/2006.01745)  
**Date:** 2020-06-02  
**Summary page:** https://www.summarizepaper.com/en/paper/2006.01745v1/

## Key points

- Lack of prevailing theory explaining origin, qualities, and alleviation of pain
- Limited use of mathematical or computational techniques in studying pain etiology and mechanisms
- Comprehensive analysis of existing mathematical and computational approaches for characterizing pain
- Identified 31 relevant articles out of 560 searched
- Predominant use of classification algorithms to categorize pain and no-pain conditions
- Emphasis on identifying presence or absence of pain rather than exploring specific features for diagnostics and treatment
- Mathematical models hold potential in augmenting understanding of pain mechanisms
- Incorporating mathematical and computational techniques can uncover new insights into pain management
- Need for further exploration and development of mathematical models in pain research
- Potential contribution to diagnostic methods and treatment strategies for various types of pain
- Further investigation necessary to identify novel pathways for mathematics' role in managing chronic or acute pains.

## Summary

In their systematic review titled "Mathematical models for pain," Victoria Ashley Lang, Torbjörn Lundh, and Max Ortiz-Catalan explore the lack of a prevailing theory that fully explains the origin, qualities, and alleviation of pain. While numerous studies have focused on molecular targets for pain management, there has been a limited attempt to investigate the etiology and working mechanisms of pain using mathematical or computational techniques. To address this gap in research, the authors conducted a comprehensive analysis of existing mathematical and computational approaches for characterizing pain. They searched databases such as Science Direct and PubMed and identified 560 articles published before January 1st, 2020. After screening these articles for inclusion of mathematical or computational models of pain, they deemed 31 articles relevant for their review. The reviewed articles predominantly employed classification algorithms to categorize pain and no-pain conditions. However, the authors observed that the literature heavily emphasized the application of existing models or machine learning algorithms to identify the presence or absence of pain rather than exploring specific features of pain that could be utilized for diagnostics and treatment. Despite being an understudied area, Lang et al. argue that the development of mathematical models holds great potential in augmenting our current understanding of pain by providing directions for testable hypotheses regarding its underlying mechanisms. By incorporating mathematical and computational techniques into pain research, researchers can potentially uncover new insights into how pain arises and how it can be effectively managed. Overall, this systematic review highlights the need for further exploration and development of mathematical models in order to advance our understanding of pain beyond traditional approaches. The authors emphasize that such models have the potential to contribute significantly to diagnostic methods and treatment strategies related to various types of pain. They suggest that further investigation is necessary in order to identify novel pathways through which mathematics can inform our knowledge about how best to manage different forms of chronic or acute pains.