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Review and Analysis of Pain Research Literature through Keyword Co-occurrence Networks

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 conducts a review and analysis of 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.


Introduction
Pain is an uncomfortable sensory and emotional experience, which serves as a symptom of various medical conditions 1-3 . Pain occurs due to multiple causes (e.g., broken bone, strained muscle, or surgery), at different body locations (e.g., back pain, muscle pain, knee pain, or chest pain), and in various forms (e.g., acute pain, chronic pain, or intermittent pain) 3 . In the United States, 20.4% of adults have chronic pain, and 7.4% of adults report that their lives are significantly impacted by chronic pain, according to the 2019 National Health Interview Survey 4,5 . Health economists estimated that the annual cost of chronic pain in the USA is around $635 billion, which exceeds the yearly cost due to diabetes ($188 billion), cancer ($243 billion), or heart disease ($309 billion) 6 . This paper is organized as follows. The background section explores the pain-related review papers published in the literature. The methods section presents an overview of the KCN-based approach to review and explore an extensive amount of literature. It also describes the data collection and data preprocessing tasks employed to build KCNs. The results and discussion section identifies and describes the emerging topics and their implications in pain research. The conclusion section summarizes the findings and limitations of this work and comments on the future direction of this work.

Background
This section reviews existing studies on pain management research from various angles. The existing literature review studies broadly fall into two categories: (1) objective pain assessment trends and (2) physiological mechanisms and treatment trends.
Objective pain assessment trends: The articles have explored automatic pain assessment [14][15][16] . In current clinical settings, patients typically self-report their pain level using the Verbal Rating Scale (VRS), Visual Analogue Scale (VAS), and Numerical Rating Scale (NRS) 17,18 . These selfreported pain measurements are subjective and often impractical when patients are not in an alert state or unable to communicate their pain level. Infants, toddlers, and adults with communication or cognitive deficits may not be able to convey their pain levels verbally. Researchers have explored the physiological signals and behavioral responses for objective pain measurement 2,19-23 . Werner et al. 24 presented a survey of automated pain recognition-related papers indexed in the Web of Science. They emphasized the advancements in non-contact and contact-based automatic pain recognition techniques that use facial expression, voice, physiology, and multimodal information. Lötsch et al. 25 published a review on machine learning in pain research to raise knowledge of the approaches in ongoing and upcoming projects. Wagemakers et al. 26 provided an in-depth analysis of the devices and methods for objectively measuring patients' pain. Chen et al. 27 reviewed various wearable physiological and behavioral sensors that may help build automated monitoring systems for pain detection in clinical settings. Zamzmi et al. 28,29 reviewed features, classification tasks, and databases for automated pain assessment in infants and provided pain assessment techniques in children considering physiological and behavioral scope.
Physiological mechanisms and treatment trends: Several researchers have tried to understand the mechanism of pain and devise methods for better treatment of a specific type of pain [30][31][32][33] . Koechlin et al. 34 presented a systematic review of the role of emotion regulation in chronic pain. They examined the risk and protective factors contributing to chronic pain management. IsHak et al. 35 examined studies that addressed pain comorbid with depression through a systematic review. They observed that depression and pain are highly related and may worsen physical and psychological symptoms. Shraim et al. 36 conducted a systematic review and synthesized classification based on the mechanism of pain in the musculoskeletal system. They evaluated methods to distinguish three categories of pain mechanisms: nociceptive, neuropathic, and nociplastic pain 37 . Urits et al. 38 conducted an exhaustive literature review of low back pain and examined the pathophysiology, diagnosing methods, and treatment strategies. Finnerup et al. 39 reviewed neuropathic pain topics with emphasis on its mechanism and treatments.
The present study addresses the timely need for an exhaustive review of the pain literature published in the last two decades. We consider that the scientific research publications reflect the emerging trends in pain research and we employed a KCN-based method to comprehensively analyze thousands of pain-related articles indexed in IEEE, PubMed, Engineering Village, and Web of Science databases. This approach addresses the challenges posed classical literature review approaches in terms of time and effort to capture insights from a vast body of literature.

Overview of the Existing Methods
In literature, a few studies utilized KCNs to analyze research fields. Lee et al. 40 developed a KCN for "regional innovation systems (RIS)" literature and collected 432 articles to investigate the development of RIS research and future research directions. They used centrality-related network features. Li et al. 41 built a KCN using the complex-network-related keywords extracted from 5,944 articles published between 1990 and 2013 to analyze the trends and relationships between knowledge elements. They evaluated the networks considering the degree, clustering coefficient, and shortest path principles to understand the evolution of the articles.
Radhakrishnan et al. 42 created a novel KCN-based method to help researchers review scientific literature. As a case study, they built KCNs for "nano-related environmental, health, and safety (EHS) risk" literature. They collected keywords from 627 papers published between 2000 and 2013. They used network parameters such as degree, strength, average weight, weighted nearest neighbor's degree, and clustering coefficient for statistical analysis of KCNs.
Yuan et al. 13 presented a KCN-based analysis of the data science trends in the manufacturing literature. They extracted keywords from a collection of 84,041 articles published between 2000 and 2020. They categorized the keywords according to the nine pillars of Industry 4.0 to understand the emerging topics in this smart manufacturing research.
Weerasekara et al. 43 reviewed the evolution of industry 4.0 for asset life cycle management for sustainability concepts using KCN-based techniques. They extracted keywords from 3,896 articles and analyzed the research trends.
A common objective of the above-mentioned studies is to help researchers understand the trends in specific literature and guide them to identify future directions. In the present work, we adopted these complex-network-related metrics from Radhakrishnan et al. 42 to build algorithms for reviewing and analyzing large-scale pain research literature in the current study.

Data Collection and Processing Procedure
This section discusses the steps of the proposed approach to extract and analyze the keywords, which are the uncontrolled terms specified by the authors of the articles.
Step 1: Select the databases of citations of research articles.
Step 2: Develop an information-extraction procedure to collect the corpus of keywords from the research articles indexed in the selected databases.
Step 3: Convert the corpus into a list of unique words using the natural language processing methods.
Step 5: Construct KCNs from the adjacency matrices and weighted adjacency matrices.
Step 6: Conduct review and analysis of pain literature using the KCNs.
Article Search. To create the KCNs of pain research articles, we searched for articles that included the term "pain" in the articles' titles and keywords (corpus) defined by the authors. These articles are quarried from IEEE, Engineering Village, and Web of Science with a scope of 2002 to 2021. The National Center for Biotechnology Information (NCBI) provides the Entrez system currently covering a variety of biomedical data among 38 databases, including the PubMed database. We wrote a python code to connect the Entrez system to request and retrieve our targeted data from the PubMed database. This search resulted in a total of 184,174 articles from PubMed. From the other three databases, we searched for the articles through manual queries. This resulted in 888, 6622, and 229,758 articles from IEEE, Engineering Village, and Web of Science, respectively. Then all the titles and keywords of these articles were extracted and prepared for the next step.
Article Screening. We removed duplicate articles within and across databases. This process eliminated 156,882 articles leaving 264,560 articles for the next stage. The majority of the redundant corpus was between Web of Science and PubMed. Approximately half of the articles from both databases were duplicates.
• Changed terms with postfix, "ly", "est", "ation", or "ment" to their root form, e.g., simplified "assessment" to "assess". • Concatenated back processed words with a blank space between words to form original keywords, e.g., concatenated "neuropath" and "pain" to form "neuropath pain". This gave us sets of keywords constructed with tokenized and stemmed words. • Dropped all the terms of length one or two letters which, to our knowledge, did not play a crucial part in our analysis. Table. Using the indexed corpus from the procedure described above, we generated a document frequency table and multiple adjacency matrices of co-occurring keywords, and a dictionary including those keywords before and after stemming. For example, we present the top 10 frequently used words before and after stemming in Table 1 Figure 1. The process of data collection, processing, and cleaning by text mining techniques.

Network
A network is a set of connected entities. In network theory, entities in the graph are named nodes or vertices, and the connections between the entities are called links or edges 44 . Throughout the study, we use "edges" and "links" interchangeably. This study creates KCNs, which explore the knowledge structure of the body of scientific literature by investigating the relations among keywords in the field 42 . In a KCN, nodes represent the keywords collected from the articles, and the edges represent the co-occurrences between pairs of keywords. An edge connects a pair of nodes (keywords) if the keywords co-occur in an article.

Figure 2
illustrates an example of a KCN. Nodes are the top five frequently used keywords in the literature between 2017 and 2021, namely, pain, chronic pain, low back pain, pain management, and neuropathic pain. If a pair of words are connected by a link, then these keywords co-occur or vice versa. The thickness of the link indicates the number of times the keywords co-occur in the pool of articles: the heavier the link, the higher the co-occurrence counts. For example, pain and chronic pain co-occurred 290 times, but low back pain and neuropathic pain co-occurred only 19 times.
In this study, the relationship between keywords does not have a direction. A link simply represents that keyword co-occurs with keyword . Therefore, a KCN is an undirected network. In addition to that, it is a weighted network since the connections among nodes have weights assigned to them, which are the number of times the keyword-pairs co-occur.
A network can be represented as an adjacency matrix; it is a symmetric matrix with a size of × , where n is the number of nodes (i.e., keywords) 44 . In the adjacency matrix, if keywords and co-occur in an article, !" = 1 , otherwise !" = 0 and the diagonal elements are assigned zero 44 . A KCN network can be treated as a weighted network by adding weights to links; in that case, the adjacency matrix becomes the weighted adjacency matrix, in which !" denotes the weight of the link connecting nodes i and j; in other words, !" indicates the co-occurrence frequency of nodes (keywords) i and j 42 . Since a KCN is undirected, !" = "! .

Network Parameters
This study aims to investigate the KCN considering the nodes' and edges' statistical features, centrality, affinity, and cohesiveness.
(1) Degree: A node's degree is the total number of direct links the node has with the other nodes 45 . It is a centrality metric and is used to measure a node's importance in graph 46 . The degree of node is defined as follows: where ! represents the set of nodes connected to node and !" denotes the element of the adjacency matrix indicating the presence or absence of the connection between node and node .
An intuitive inference about the degree is that nodes with more connections are more central to the network. However, in a weighted graph, this is not always the case. In addition to the links, the weights of the links are to be considered.
(2) Strength: A node's strength is the sum of the weights of all links connected to the node. It indicates the importance of a node considering both degree and weight 47 . The node's strength of a node is calculated as follows: where ! is the set of nodes connected to node ; !" is the weight of the link between nodes and in the weighted adjacency matrix.
(3) Average weight as a function of endpoint degree: The average weight as a function of endpoint degree indicates changes in the frequency of cooccurrence of the edges between pairs of nodes as the product of the degrees of end nodes of edges changes 13,42 . It determines the relative change in the edge weights as the number of edges connected to the end nodes changes 48 . The endpoint degree of an edge between node and node is defined as the product of degrees of nodes connected to the edge, i.e., ! " . Let !" be the set weights of all edges whose endpoint degree is equal to ! " .
where and n is the total number of nodes in the network. The average weight 〈 !" 〉 is defined as the average of all weights %& ∈ !" .
where, 4 !" 4 is the cardinality of the set. Here is an example how to implement the Eq. 3: Step 1: Find degree of each node e.g., ) = 1, * = 60, + = 20, , = 3 The average weight as a function of end point degree explains the nature of association between nodes of different degrees. If < !" > increases with ! " , links among the keywords with highdegree are more prevalent than the links among the keywords with low-degree. Conversely, if < !" > reduces with ! " , links between the keywords with low-degree are more prevalent than links between the keywords with high-degree.
(4) Average weighted nearest neighbor's degree as a function of the degree: The average weighted nearest neighbor's degree as a function of the degree measures the affinity among a node's direct neighbors. It demonstrates if a node in the network has similar network characteristics as its neighbors in terms of degree. An increasing trend in this function implies that nodes with high-degree are prone to bind to other nodes with high-degree, in which case the network has assortative behavior. On the other hand, a decreasing trend shows that nodes with high-degree bind mostly to nodes with low-degree, in which case the network exhibits disassortative behavior 47 . It is calculated as follows: where ! is the node strength, ! is the set of nodes connected to node , !" is the weight of the link between node and , and " is the degree of node .
(5) Weighted clustering coefficient as a function of degree: The weighted clustering coefficient quantifies the local cohesiveness of a node; it characterizes the node's connection density to its neighbors 42,47 . In the current study, since the network is a weighted graph, the geometric average of the subgraph edge weights is used to define the clustering coefficient 49 .
where ! is the degree of node , ! is the set of nodes connected to node , the maximum weight in the network N !" = !" /max( ) normalizes the weights !" 50 . If the multiple nodes have the same degree, then the weighted clustering coefficient corresponding to the degree is averaged over the nodes. In other words, the multiple weighted clustering coefficients corresponding to a degree are averaged. After computing the weighted clustering coefficient using Eq. 5, it is plotted as a function of degree.

Results and Discussion
This work aims to analyze the knowledge components, structure, and research trends in the pain literature. We organized the results and discussion into four subsections: (1) Frequently used pain-related keywords, (2) KCN analysis of pain-related literature, (3) Pain research trends, and (4) Association patterns among pain-related keywords.

Frequently Used Pain-Related Keywords
The strength metric (see Eq. 2) indicates a keyword's popularity considering the total count of co-occurrences with other keywords. Using strength, Table 2 (a) illustrates the top 20 keywords ranked by their strength in the 2017-2021 time window. It is evident that in this time window, the researchers concentrated on pain, chronic pain, pain management, low back pain, neuropathic pain, and opioid concepts. Table 2 (b) demonstrates the top 20 frequently co-occurring keyword pairs. In the years 2017 through 2021, researchers were interested in synergistic topics of "pain, opioid," "pain, analgesia," "pain, anxiety," "pain, quality," and "pain, depression." Opioid and analgesia ranked positions 1 and 2 when it comes to their association with pain; in addition, opioid and analgesic co-occurred considerably as well. A similar three-way trend is observed among anxiety, depression, and pain.    Co-occurrence  pain  66282  pain, opioid  881  chronic pain  22376  pain, analgesia  756  pain management  13413  pain, anxiety  688  low back pain  13037  pain, quality life  681  neuropathic pain  12768  pain, depression  610  opioid  11354  pain, osteoarthritis  544  analgesia  7508  depression, anxiety  532  quality life  6690  pain, inflammation  531  postoperative pain  6544  chronic pain, opioid  498  depression  6515  pain, cancer  442  anxiety  5863  pain management, opioid  426  osteoarthritis  5158  chronic pain, pain management  392  back pain  4942  pain, postoperative  360  inflammation  4655  pain, analgesic  341  abdominal pain  4484  pain, fatigue  335  rehabilitation  4457  pain, pain management  317  neck pain  4144  pain, nociceptive  306  analgesic  3908  opioid, analgesic  292  acute pain  3749  chronic pain, pain  290  systematic review  3544 pain, fibromyalgia 281

KCN Analysis of Pain-Related Keywords
The summary of the topological properties of KCNs of four time-windows is presented in Table  3 and Figure 5, and Figure 5. The increasing trends in the average degree and average strength confirm the introduction of new keywords to the pain literature from diverse research fields (see Figure 5). The maximum degree and the maximum strength show that the keyword "pain" has formed strong connections with other keywords in the pain-related literature.
Average network weight is calculated as the sum of the weights of all links in the network divided by the total number of links. This metric remained almost the same over the four timewindows. Slight fluctuations in the average weight are due to different growth patterns in the sum of weights and the number of links. On the other hand, maximum weight has a study of growth over time.     The skewed distribution reveals that the cross-fertilization of topics has become extensive in recent years (2017-2021). In addition, many new keywords were introduced to the literature, which were listed only 1 or 2 times in the literature and did not form many associations with other keywords. This is the reason why the median of the weight distribution is 1. The "average weight as a function of endpoint degree" is shown in Figure 7 (a). It shows an increasing trend with time. The increasing trend for this metric indicates that high-degree keywords are more likely to co-occur with other high-degree keywords. Additional network properties need to be examined in parallel to confirm this conclusion. The high value for ! " (degree of node times degree of node ) may have resulted from two different possibilities: the multiplication of two high-degree nodes (e.g. ! " = 100*100 = 10,000 ) or the multiplication of one ultra-high-degree node and one low-degree node (e.g. ! " = 10,000*1 = 10,000). The first case would happen when researchers frequently use the same pair of keywords (e.g., pain and opioid, pain and analgesia). The second case would happen when researchers synergize a highly popular topic with an emerging topic (e.g., pain and big data, pain and neural networks). The association between high and low-degree nodes needs to be checked to differentiate between these two cases. Therefore, the "average weighted nearest neighbor's degree" metric is examined to understand if a node and its neighbors have similar network characteristics in terms of degree. This flat trend suggests the absence of any significant topological relationship between the "average weighted nearest neighbor's degree" and "degree." These observations contradict the fact that high-degree nodes are more likely to bind to the other high-degree nodes, and low-degree nodes bind to the other low-degree nodes. The reason is that the "average weighted nearest neighbor's degree" demonstrates that nodes do not have similar network characteristics as their neighbors regarding degree. It indicates that high-degree keywords do not only bind with other high-degree but they also bind with the low-degree nodes. For instance, the "pain" keyword is the highest-degree node which connects 11,532 other keywords. It links to the other high-degree nodes like chronic pain, opioid, and pain management as well as to 2,393 low-degree nodes like agent-based modeling, ankle surgery, lavender oil, traumatic stress, and sexual assault, whose degree is less than 20.
Not only does the highest-degree node, which is the "pain," associate with other high-degree nodes such as "chronic pain," "back pain," and "opioid," but also with new emerging topics such as "machine learning," "neural network," "regression," and "measurement." The patterns in Figure 7 (b) confirm that new topics and connections have emerged in the pain literature over time.  Figure 8 shows the weighted clustering coefficient across different values of degree. Over the four time-windows, there is a declining trend in the clustering coefficient, indicating that nodes with a small degree constitute dense clusters more with other small-degree nodes than with highdegree ones. However, high-degree nodes have strong connections with both other high-degree and low-degree nodes. In addition, the clustering coefficient of the 2017-2021 period is always less than that of other time windows. It is evident that in recent years, some nodes have grown as high-degree nodes. For instance: "chronic pain" is one of the highest-degree keywords, and over time, the numbers of keywords connected to "chronic pain" are 303, 738, 2422, and 4611, respectively, in the 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. Table 4 demonstrates the keywords that have the highest connection density to the neighbor keywords from 2017 to 2021, as measured by the weighted clustering coefficient (See Eq. 5).
"Clinical decision pathway, noncardiac, intensive critical care, chest pain syndrome, and aha clinical practice guidelines" topics are frequently used with a similar set of keywords in the articles, such as "obesity, sleep, physical activity, lifestyle, chronic pain, diet, cancer survivor, stress, and psychological factor."

Pain Research Trends
To further analyze the evolution of pain literature visually, each keyword is placed in one of three following categories for the only purpose of easy visualization: • Sensors/methods-related keywords (e.g., electromyography, biomarker, machine learning) keywords • Biomedical-related keywords (e.g., chronic pain, back pain) • Treatment-related keywords (e.g., surgery, acupuncture)  For the past two decades, the research community has investigated pain assessment and pain management approaches. The full potential of pain data was largely untapped in 2002-2006 period because machine learning methods were not actively explored in pain research. Machine learning and statistical modeling techniques have been widely applied to pain assessment and management, starting with the 2012-2016 time-window. Pain researchers explored model building (e.g., deep learning, support vector machines), decision making (e.g., treatment, patient quality of life), and pain measurement (e.g., machine learning, biomarker). However, other modeling methods, such as linear regression, have also been quite common since 2002. We observed that sensors and predictive models have become more common in pain assessment and management.  Figure 10 shows the trends in the biomedical category. The data reveals that acute pain, musculoskeletal pain, chronic low back pain, cancer pain, and postoperative pain have become increasingly popular over the years and have been actively studied in the last two decades. Meanwhile, chronic pain, neck pain, abdominal pain, low back pain, neuropathic pain, analgesia, back pain, and inflammation have retained their rank on the frequency list and have stayed as the focus of the researchers all through. Dorsal root ganglion, visceral pain, capsaicin, spinal cord, nociceptive, and allodynia have lost popularity over the last two decades.  11 presents the trends in the treatment category. Opioids, rehabilitation, analgesics, surgery, exercise, acupuncture, and morphine treatments have remained active research topics in recent years. Laser and laser-evoked potential treatment techniques have lost researchers' attention over time. Meanwhile, researchers have directed their interest toward self-management, cognitive behavioral therapy, physical activity, and opioid analgesic topics. The significant changes and trends in keywords since 2002 also reveal the emergence of new technologies for pain management. The early work in the medical field for pain management and assessment has focused on treatment methods such as chemotherapy, but the current research has been emphasizing patient wellness. With the availability of large-scale personal datasets and the development of wearable instruments, the recent work has focused on the quality of life of individual patients, self-management of pain, and plethora of therapy methods.

Association Patterns among Pain-Related Keywords
We have conducted affinity analysis 51 on pain-related articles. Using the Apriori algorithm 52,53 , the affinity analysis first finds all combinations of items (also referred to as itemsets) that occur in a large set of transactions with probabilities greater than a desired threshold. Next, the affinity analysis formulates the rule of co-occurrence of items in the transaction set. Typically, the rules take the form "IF antecedent itemset THEN consequent itemset." The strength of each rule is assessed using several measures, such as support count, confidence, and lift. In Table 5, we present these measures for the top 15 rules ranked by their lift. In our study, the support count of a rule is the number of pain-related articles (published from 2002 to 2021) that contain both the antecedent and the consequent keyword(s). The confidence of a rule is the conditional probability of the consequent keyword(s) appearing in the article set, given the presence of antecedent keyword(s) in the article set. The lift of a rule indicates the strength/efficiency of the rule informing the occurrence of the consequent keyword(s). In other words, lift is the ratio of the chance of seeing the consequent keyword(s) in an article if we use the rule to the chance of seeing the consequent keyword(s) without the insights from the rule. With lift greater than 1, we have a greater chance of seeing the consequent keyword(s) in the literature if we know that the antecedent keyword(s) appeared in the literature. We set the minimum support count as 200 and the minimum confidence as 0.55. These settings are determined using computational and practical considerations. The Apriori algorithm yielded 15 association rules that meet the support count and confidence criteria set by the authors ( Table  5).
To illustrate the results above, consider the first row in Table 5. It presents the rule "IF bladder pain syndrome, THEN interstitial cystitis." It means, if bladder pain syndrome appeared in an article, then interstitial cystitis will also appear in the article with a confidence of 0.78 and a lift of 425.53. Similarly, the second row in the table leads to the following rule: "IF acute coronary syndrome, THEN chest pain," with a confidence of 0.56 and a lift of 79.07. These rules inform the researchers on the association among keywords or, in other words, affinity among topics in \ pain-related articles.

Conclusion
Pain is an important medical issue that affects millions of people every day. Pain-related research has grown extensively across different fields in the last two decades. The vast amount of literature on pain-related research makes the traditional literature review process tedious and impractical. This study, using a KCN approach, provides a macro-level picture of the current pain literature.
In this study, we collected 264,560 articles published between 2002 and 2021 from IEEE, Web of Science, PubMed, or Engineering Village by comprehensively searching pain research articles. We extracted all the keywords from these articles and applied data cleaning and text processing techniques to remove duplicates, tokenize keywords, and extract stem words. From these keywords, we constructed adjacency matrices and weighted adjacency matrices. Using these matrices, we built KCNs and analyzed the network features, such as centrality, affinity, and cohesiveness, to understand the knowledge components, knowledge structure, research trends, and emerging research topics.
Through the KCN-based analysis, we observed that pain literature had grown tremendously in the past two decades. The number of articles and keywords has grown by a factor of 3 and 7, respectively. The number of co-occurrences of keywords has grown at more than twice the speed of keywords growth. We identified the emerging and declining topics in the following categories: (1) sensors/methods, (2) biomedical, and (3) treatment. The Results and Discussion section presented the research trend and insights.
The categorization process of the keywords as sensors/methods, biomedical, and treatment has limitations. This study did classify a keyword into only one of the three categories; it did not consider overlapping membership in the categories. Moreover, the categorization for the purpose of visualization of the emerging and declining trends was performed manually banking on the domain knowledge of the authors. Therefore, the categorization may likely to have subjectivity. Future work will consider classifying a keyword into more than one category and expanding the categories in addition to sensors/methods, biomedical, and treatment. It will automate the classification of a keyword in categories using built-in dictionaries in Python, such as PyMedTermino. In addition, future work will expand the keywords by extracting all words from the abstract. In future work, the KCN-based methods will be extended to analyze the connections between the authors of the articles to reveal potential collaboration patterns across pain-related fields.