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Decade of Natural Language Processing in Chronic Pain: A Systematic Review

Swati Rajwal

TL;DR

This systematic review synthesizes 2014–2024 research on NLP approaches in chronic pain, identifying 26 peer‑reviewed studies across clinical, linguistic, and computational domains. It documents a methodological shift from rule-based and traditional ML methods to transformer-based models and emerging large language models, with several studies achieving strong classification performance on diverse data sources including EHRs and social media. The review also reveals persistent gaps in dataset diversity, standard evaluation metrics, and reproducibility, underscoring the need for cross‑institute validation, open science practices, and multilingual research to ensure equitable impact. Collectively, the work outlines concrete directions to strengthen methodological rigor and clinical relevance of NLP tools in chronic pain management and research.

Abstract

In recent years, the intersection of Natural Language Processing (NLP) and public health has opened innovative pathways for investigating various domains, including chronic pain in textual datasets. Despite the promise of NLP in chronic pain, the literature is dispersed across various disciplines, and there is a need to consolidate existing knowledge, identify knowledge gaps in the literature, and inform future research directions in this emerging field. This review aims to investigate the state of the research on NLP-based interventions designed for chronic pain research. A search strategy was formulated and executed across PubMed, Web of Science, IEEE Xplore, Scopus, and ACL Anthology to find studies published in English between 2014 and 2024. After screening 132 papers, 26 studies were included in the final review. Key findings from this review underscore the significant potential of NLP techniques to address pressing challenges in chronic pain research. The past 10 years in this field have showcased the utilization of advanced methods (transformers like RoBERTa and BERT) achieving high-performance metrics (e.g., F1>0.8) in classification tasks, while unsupervised approaches like Latent Dirichlet Allocation (LDA) and k-means clustering have proven effective for exploratory analyses. Results also reveal persistent challenges such as limited dataset diversity, inadequate sample sizes, and insufficient representation of underrepresented populations. Future research studies should explore multimodal data validation systems, context-aware mechanistic modeling, and the development of standardized evaluation metrics to enhance reproducibility and equity in chronic pain research.

Decade of Natural Language Processing in Chronic Pain: A Systematic Review

TL;DR

This systematic review synthesizes 2014–2024 research on NLP approaches in chronic pain, identifying 26 peer‑reviewed studies across clinical, linguistic, and computational domains. It documents a methodological shift from rule-based and traditional ML methods to transformer-based models and emerging large language models, with several studies achieving strong classification performance on diverse data sources including EHRs and social media. The review also reveals persistent gaps in dataset diversity, standard evaluation metrics, and reproducibility, underscoring the need for cross‑institute validation, open science practices, and multilingual research to ensure equitable impact. Collectively, the work outlines concrete directions to strengthen methodological rigor and clinical relevance of NLP tools in chronic pain management and research.

Abstract

In recent years, the intersection of Natural Language Processing (NLP) and public health has opened innovative pathways for investigating various domains, including chronic pain in textual datasets. Despite the promise of NLP in chronic pain, the literature is dispersed across various disciplines, and there is a need to consolidate existing knowledge, identify knowledge gaps in the literature, and inform future research directions in this emerging field. This review aims to investigate the state of the research on NLP-based interventions designed for chronic pain research. A search strategy was formulated and executed across PubMed, Web of Science, IEEE Xplore, Scopus, and ACL Anthology to find studies published in English between 2014 and 2024. After screening 132 papers, 26 studies were included in the final review. Key findings from this review underscore the significant potential of NLP techniques to address pressing challenges in chronic pain research. The past 10 years in this field have showcased the utilization of advanced methods (transformers like RoBERTa and BERT) achieving high-performance metrics (e.g., F1>0.8) in classification tasks, while unsupervised approaches like Latent Dirichlet Allocation (LDA) and k-means clustering have proven effective for exploratory analyses. Results also reveal persistent challenges such as limited dataset diversity, inadequate sample sizes, and insufficient representation of underrepresented populations. Future research studies should explore multimodal data validation systems, context-aware mechanistic modeling, and the development of standardized evaluation metrics to enhance reproducibility and equity in chronic pain research.

Paper Structure

This paper contains 47 sections, 9 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Rationale
  3. Objectives
  4. Methods
  5. Eligibility Criteria
  6. Information Sources
  7. Search Strategy
  8. Selection Process
  9. Data Collection Process
  10. Data Items
  11. Study risk of bias assessment
  12. Reporting bias assessment
  13. Study Records
  14. Ethical Considerations
  15. Results
  16. ...and 32 more sections

Figures (9)

  • Figure 1: PRISMA flow diagram.
  • Figure 2: Number of articles per year considered in this review.
  • Figure 3: Summary of the 26 included Studies.
  • Figure 4: Summary of the 26 included studies, cont'd.
  • Figure 5: PubMed Search Results
  • ...and 4 more figures