Analysis of pain research literature through keyword Co-occurrence networks

Abstract

Pain is a significant public health problem as the number of individuals with a history of pain globally keeps growing. In response, many synergistic research areas have been coming together to address pain-related issues. This work reviews and analyzes a vast body of pain-related literature using the keyword co-occurrence network (KCN) methodology. In this method, a set of KCNs is constructed by treating keywords as nodes and the co-occurrence of keywords as links between the nodes. Since keywords represent the knowledge components of research articles, analysis of KCNs will reveal the knowledge structure and research trends in the literature. This study extracted and analyzed keywords from 264,560 pain-related research articles indexed in IEEE, PubMed, Engineering Village, and Web of Science published between 2002 and 2021. We observed rapid growth in pain literature in the last two decades: the number of articles has grown nearly threefold, and the number of keywords has grown by a factor of 7. We identified emerging and declining research trends in sensors/methods, biomedical, and treatment tracks. We also extracted the most frequently co-occurring keyword pairs and clusters to help researchers recognize the synergies among different pain-related topics.

Fig 1. The process of data collection, processing, and cleaning by text mining techniques.

Fig 2. The KCN for the five most frequently used keywords during 2017–2021.

Nodes (entities) are the keywords of the articles, edges (links) are the co-occurrences of pairs of keywords, and the link thickness denotes the number of times the keywords co-occur in the pool of articles.

Fig 3. Network of the top 20 keywords ranked by strength during 2017–2021.

Nodes are the keywords; edges are the co-occurrences of the pairs of keywords.

Fig 4. Number of articles, number of keywords, and number of co-occurrences over the four time windows.

Fig 5. Node degree, node strength, and link weights over the four time windows.

Fig 6

The distribution of the KCN A degree, B strength, and C weight.

Fig 7

A Average weight as a function of endpoint degree, B Average weighted nearest neighbor’s degree as a function of the degree. Both the x-axis and the y-axis are on the logarithmic scale. The primary takeaway is that high-degree nodes have connections with both other high-degree nodes and low-degree nodes since nodes do not have similar network characteristics as their neighbors in terms of degree.

Fig 8. Weighted clustering coefficient as a function of degree for four time windows.

Both the x-axis and the y-axis are on the logarithmic scale. The main takeaway is that nodes with smaller degrees constitute more dense clusters with other smaller degree nodes; however, nodes with high-degree have a strong connection with both nodes with high-degree and low-degree.

Fig 9. Emerging (left panel) and declining (right panel) keywords in the sensors/methods category from 2002–2006 to 2017–2021.

Numbers next to keywords represent the rank, and numbers in parentheses represent the frequency of keywords. The total number of unique keywords is 1,534 between 2002 and 2006 and 11,532 between 2017 and 2021.

Fig 10. Emerging (left panel) and declining (right panel) keywords in the biomedical category from 2002–2006 to 2017–2021.

Numbers next to keywords represent the rank, and numbers in parentheses represent the frequency of keywords. The total number of unique keywords is 1,534 between 2002 and 2006 and 11,532 between 2017 and 2021.

Fig 11. Emerging (left panel) and declining (right panel) keywords in the treatment category from 2002–2006 to 2017–2021.

Numbers next to keywords represent the rank, and numbers in parentheses represent the frequency of keywords. The total number of unique keywords is 1,534 between 2002 and 2006 and 11,532 between 2017 and 2021.