A temporal ontology is created to describe the calcification process in breast cancer. A terminological comparison of three ontological descriptions of the calcification process is carried out over three time periods (2003–2009, 2010–2015, 2016–2021) considering the emergence of new terms. Candidate terms for inclusion in the ontology were programmatically selected from a collection of documents and approved by experts for inclusion in the ontology, considering their contextual meanings. Three ontological representations for describing the process of calcification contain concepts related to the process of calcification in breast cancer, have a hierarchy of 103 concepts, and are accompanied by a set of definitions that define these concepts. For each period, an ontology is built for term-like phrases for each period, and the relationships between terms are clarified based on the medical literature. The temporal ontology of terms for the calcification process revealed the most actively developing ontology branches.

Purpose The landscape of health information acquisition has shifted from offline to online, and online question-and-answer (Q&A) communities have emerged as prominent sources of health information; however, it is unclear how users identify satisfactory health information. This paper identifies factors that influence users’ adoption of health information in the context of online Q&A communities. Design/methodology/approach Based on the elaboration likelihood model (ELM) and opinion leader theory, we construct a research model to examine how information quality (complexity, image structure and emotional change) and source credibility (authentication status, follower number) affect health information adoption behavior. We verify the hypotheses by Poisson regression and zero-inflation Poisson regression using the data collected from an online Q&A community. Findings The empirical results indicate that both information quality and source credibility positively affect users’ adoption of health information. Originality/value This research can assist designers and managers of online Q&A communities to better comprehend users’ health information needs and their preferences for adoption. This enhanced understanding can facilitate the provision of superior online health information.

Purpose This study aims to introduce a novel methodology for visually analyzing psychological tension in social networks, particularly in the context of disturbances related to coronavirus vaccination. It also aims to enhance the interpretation of online discourse dynamics by integrating mathematical, linguistic and visual-analytical methods. Design/methodology/approach The study uses a comprehensive approach, including tweet array generation via the Vicinitas API, key term extraction, sentiment analysis and visualization tools such as Word-Cloud, VosViewer, Gephisto and Gephi. This methodology is tested on protests against coronavirus vaccination to evaluate its effectiveness in capturing the intricacies of digital discourse. Findings The study identifies key themes, including psychological stress linked to vaccination, protest movements and sentiments regarding trust and belief. Hierarchical representations and visualizations reveal the nuances within digital discourse, demonstrating the methodology’s capacity to discern patterns in social media interactions. Practical implications Practically, this methodology offers a robust tool for monitoring and interpreting online discourse, particularly in scenarios demanding immediate response, such as public health crises. Public health officials can use this method to detect early signs of misinformation or psychological distress, enabling timely interventions. Policymakers and analysts can leverage these insights to design communication strategies that build trust and mitigate public anxiety, aligning with societal needs for transparency and accountability. Originality/value This research introduces a novel integration of computational tools and expert insights, specifically designed for real-time analysis of psychological tension in online discourse. Unlike previous methods that focus on text analysis or sentiment evaluation independently, this approach uniquely combines sentiment dynamics with predictive modeling, offering a comprehensive lens for understanding digital interactions during sensitive public health crises.

PurposeEffective communication is crucial in the medical field where different stakeholders use various terminologies to describe and classify healthcare concepts such as ICD, SNOMED CT, UMLS and MeSH, but the problem of polysemy can make natural language processing difficult. This study explores the contextual meanings of the term "pattern" in the biomedical literature, compares them to existing definitions, annotates a corpus for use in machine learning and proposes new definitions of terms such as "Syndrome, feature" and "pattern recognition."Design/methodology/approachEntrez API was used to retrieve articles form PubMed for the study which assembled a corpus of 398 articles using a search query for the ambiguous term "pattern" in the titles or abstracts. The python NLTK library was used to extract the terms and their contexts, and an expert check was carried out. To understand the various meanings of the term, the contextual environment was analyzed by extracting the surrounding words of the term. The expert determined the appropriate size of the context for analysis to gain a more nuanced understanding of the different meanings of the term pattern.FindingsThe study found that the categories of meanings of the term "pattern" are broader in biomedical publications than in common definitions, and new categories have been emerging from the term's use in the biomedical field. The study highlights the importance of annotated corpora in advancing natural language processing techniques and provides valuable insights into the nuances of biomedical language.Originality/valueThe study's findings demonstrate the importance of exploring contextual meanings and proposing new definitions of terms in the biomedical field to improve natural language processing techniques.

GT outcomes related to COVID-19 and insomnia are reviewed. Peaks in requests are noted during disease outbreaks. A machine-expert analysis of the vocabulary of tweets showed that in November 2021, users mainly complained of feeling unwell, insomnia, and headaches. In November 2022, problems related to the brain became important topics of discussion. Users actively discussed sleep problems, often using emoji. Comparison of the vocabulary of scientific articles and tweets showed a significant similarity of the topics discussed. Twitter users often complain about poor sleep and insomnia due to current COVID-19, or a previous illness (long-term consequences of COVID-19). At the same time, problems associated with the brain and neuropsychological state are often noted. At the same time, these topics are poorly represented in the scientific literature. Consequently, Twitter users may be exposed to distorted information, which may exacerbate their psychological stress.

The coronavirus pandemic (COVID-19) has created challenging working conditions in coal-production activities. In addition to the massive loss of resources for miners, it has had a devastating impact on these individuals’ mental health. Based on the conservation of resources (COR) theory and a resource-loss perspective, this study examined the impact of COVID-19 risk, life-safety risk, perceived job insecurity, and work–family conflict on miners’ job performance. Moreover, this study investigated the mediating role of job anxiety (JA) and health anxiety (HA). The study data were collected through online structured questionnaires disseminated to 629 employees working in a coal mine in China. The data analysis and hypothesis generation were conducted using the structural equation modeling (partial least squares) method. The results demonstrated that the perception of COVID-19 risk, life-safety risk, job insecurity, and work–family conflict negatively and significantly impacted miners’ job performance. In addition, JA and HA negatively mediated the relationships between the perception of COVID-19 risk, life-safety risk, perceived job insecurity, work–family conflict, and job performance. The findings of this study can give coal-mining companies and their staff useful insights into how to minimize the pandemic’s effects on their operations.

Background: Although acne is one of the most common diseases, medical literature lacks a scientometric analysis relevant to this disorder. In the present study we aimed to perform a scientometric evaluation of literature related to acne.

This article address approaches to the development of methods for assessing the psychological state of social network members during the coronavirus pandemic through sentiment analysis of messages. The purpose of the work is to determine the psychological tension index by using a previously developed thematically ranked dictionary. Researchers have investigated methods to evaluate psychological tension among social network users and to forecast the psychological distress. The approach is novel in the sense that it ranks emojis by mood, considering both the emotional tone of tweets and the emoji's dictionary meanings. A novel method is proposed to assess the dynamics of the psychological state of social network users as an indicator of their subjective well-being, and develop targeted interventions for help. Based on the ranking of the Emotional Vocabulary Index (EVI) and Subjective Well-being Index (SWI), a scheme is developed to predict the development of psychological tension. The significance lies in the efficient assessment of the fluctuations in the mental wellness of network users as an indication of their emotions and a prerequisite for further predictive analysis. The findings gave a computed value of EVI of 306.15 for April 2022. The prediction accuracy of 88.75% was achieved.

Background: Acne or acne vulgaris is the most common chronic inflammatory disease of the sebaceous follicles. Objectives: The present study aims to identify the main lines of research in the field of acne treatment using reproducible scientometric methods. In this article, we reviewed the following research trends: facial acne, different antibiotics, retinoids, anti-inflammatory drugs, epidermal growth factor receptor inhibitors therapy, and associated diseases. Methods: The analysis of publications from the PubMed collection was carried out from 1871 to 2022. All data were analyzed using Microsoft Excel. The evolution of the terminological portrait of the disease is shown. Results: Trends in the use of various groups of antibiotics, retinoids, anti-inflammatory drugs, and photodynamic therapy for acne treatment have been found. There is a growing interest in clindamycin and doxycycline (polynomial and exponential growth, respectively). The effects of isotretinoin are also being studied more frequently (active linear growth). The publication of studies on spironolactone is increasing (linear growth). There is also a steady interest in the use of epidermal growth factor receptor inhibitors in the recent years. There is active research on acne and polycystic ovary syndrome (exponential growth). Limitations: Only articles in English were selected. The most frequent terms were considered. Conclusions: The dynamics of publication activity in the field of acne was considered. The aim of the current scientometric study was to analyze the global trends in acne treatments. The trend analysis made it possible to identify the most explored areas of research, as well as indicate those areas in dermatology in which interest is declining.

Background: Currently, there is only one bibliometric study of lichen planus (LP) and oral lichen planus (OLP) in the literature, which examined the most cited articles in the Scopus database. Our study covered all published articles in the PubMed database for 140 years since, from 1880 to 2021. In addition to the classical bibliometric analysis, we conducted a lexical analysis of key terms to build research trends in the oral lichen planus. Aims: Analysis of publication activity in the field of lichen planus by countries, their economic status and population, as well as identification of concomitant diseases by lexical analysis of key terms extracted from headings and abstracts over the past 20 years. Methods: Information from the PubMed database was retrieved automatically based on a query for the period from 1880 to 2021, including the field: title, abstract, authors, and year of publication. A total of 8173 articles were retrieved. The selection of keywords and identifying trends in related terminology were done using expert and automatic methods. An analysis of publication activity by country and socio-economic indicators was carried out. With the help of neural network analysis, the most characteristic terms related to LP were identified. Common terms were ranked by occurrence in titles and abstracts. Results: Publication activity in the field of LP and OLP has especially increased in the 21st century. The United States is the most productive country. China is a leader among countries with economies in transition. India is a leader among emerging economies. LP research is distributed worldwide. Finland ranks first in the number of publications per capita. The temporal dynamics of terminology are noted, including an increase in the number of terms used in any field of science (hereinafter referred to as general scientific terms). Conclusion: Publication activity in the field of LP and OLP has increased significantly in the 21st century. The highest publication activity was observed among authors from India, the USA and China. The leaders among economically developed countries are Italy, among the countries with economies in transition - China, and among the emerging economies - India. Based on lexical analysis of key terms, the following concomitant diseases were identified: carcinoma, leukoplakia, hepatitis, and lupus.

Aims: The purpose of the study was to develop principles of a strategy for influencing the psychological state of social network users using the example of the Russian-language segment of Twitter, one of the reasons for which is the lack of awareness about aspects of the coronavirus infection. Background: In contrast to the existing works on mood management and Emotion Regulation Strategies, there are principles based not on emotional regulation (cognitive reappraisal and expressive suppression), but on information processing of the content of social media messages and forums. Objective: The objective of the study was to develop principles of a strategy for reducing the psychological tension of social network users (further – Strategy) based on the Russian-language segment of Twitter. Methods: The proposed research methodology includes a study of the discussion field in the active forum of the Runet (the qualitative aspect of emotionality as a reflection of psychological tension) and the Russian-language segment of Twitter (the quantitative aspect of terminology frequency). The qualitative research consisted in isolating the sensitive words used by vaccine opponents to describe their beliefs. A multi-stage methodology has been developed for the meaningful analysis of Twitter users’ messages. Results: . The result of the study is a methodology for developing principles of the Strategy. Based on this methodology, the following aspects of the problem have been developed: 1) the principle of clarifying the definition of psychological tension; 2) the principle of comparing the user and scientific meanings of terms, taking into account the contexts of their use; 3) the principle of contextual comparison of the user’s and scientific meanings of the term; 4) the principle of visual popularization of scientific knowledge. Conclusion: An original methodology was created for developing principles of the Strategy. In contrast to the existing works on mood management and Emotion Regulation Strategies, there are principles based not on emotional regulation (cognitive reappraisal and expressive suppression [1]), but on information processing of the content of social media messages and forums. Other: A new approach to reducing the psychological tension of social media users can contribute to sharing timely, accurate and positive information about COVID-19, and reduce excessive discussions about COVID-19, which can positively affect the psychological well-being of the general public.

Abstract The aim of the study is to form a set of meaningful terms to improve the precision of searches in the PubMed scientific library for information related to the early stage of breast cancer. The proposed methodology included several stages. First, an experimental array of documents retrieved from PubMed database was generated. Then two groups of terms were formed based on the documents of the array. The terms of the first group referred to the general description of the disease (general terms). The terms of the second group referred to its early stage (original terms). The terms of both groups were divided into seven categories. Next, an expert comparison was made between both groups of terms in each category. Using the methodology, the following results were obtained. The most significant terms for the general course of the disease and for the early stage were identified. The terminological portrait of breast cancer including two corresponding parts (general course and early stage) was created. The main novelty of the results obtained lies in the selection out original terms of the terminological portrait for each category. An increase in the search precision is shown when using original terms in comparison with a search based on the general terms. The using of original terms will allow medical users to retrieve the documents related to the early stage of disease.

Abstract The purpose of the study was to develop the methodology identifying and tracking social media misinformation in tweets about the impact of the coronavirus and COVID-vaccine on reproductive health, one of the reasons for which is the lack of awareness about aspects of the coronavirus infection. We use a combination of machine and expert methods, and use the latest scientific articles as the standard for detecting disinformation. The proposed methodology includes the study of scientific articles as a source of reliable truthful information about the topic (information standard) and Twitter messages (assessment of information compliance with the standard). The result of the study is a methodology for detecting disinformation in the messages of social network users. Based on this methodology, the following aspects of the problem have been developed: 1) the formation of a scientific standard; 2) the principle of comparing the directions of scientific research and discussions on Twitter; 3) the principle of contextual comparison of user and scientific ideas about problems. An original methodology for identifying disinformation in social networks is proposed. In contrast to existing works, principles based on the processing of information from the content of scientific articles and messages from social networks are formulated.

COVID-19 (coronavirus disease 2019) vaccines have become available; now, everyone has the opportunity to get vaccinated. We used Google Trends (GT) data to assess the global public interest in COVID-19 vaccines during the pandemic. For the analysis, a period of 17 months was chosen (from Jan 19, 2020, to Jul 04, 2021). Interest in user queries was tracked by keywords (corona vaccine, COVID-19 vaccine development, Sputnik v, Pfizer vaccine, AstraZeneca vaccine, etc.). The geographic analysis of queries was also carried out. The interest of users in the vaccine is significantly increasing. It is focused on the side effects of vaccines, and users pay attention to vaccines’ developers from different countries. The correlation between the scientific publications devoted to vaccine development and such requests of users on the internet is absent. This study shows that internet search patterns can be used to gauge public attitudes towards coronavirus vaccination. Safety concerns consistently high follow an interest in vaccine side effects. This data can be used to track and predict attitudes towards vaccination of populations from COVID-19 in different countries before global vaccination becomes available to help mitigate the adverse effects of the pandemic.

The article provides an analysis of the terminology associated with novel coronavirus based on the study of the terms of scientific publications and social media messages by ITO and ITO-Sent models. These two models are outcomes of digitalisation of the SECI model. There are three main differences between these two models and the SECI model. First, first two models distinguish between two knowledge representation forms: words and computer codes. Second, they include four transition processes: visualisation (computer codes -> words), digitalisation (words -> computer codes), conceptualisation, and annotating, apart from four processes already present in the SECI model (socialisation, externalisation, combination, internalisation). Third, they serve as a theoretical basis for developing information technology for the goal-oriented discovery of new knowledge in texts. An information technology has been developed to discover traditional and new medical terms in the texts of scientific publications and Twitter messages associated with novel coronavirus.

Aim: The aims of the research were to study the citation history of popular articles in the field of biomarkers in personalized medicine, to study the use of terms in the sections of articles, and to consider the key terminology of the most-cited articles and its visualization. Background: The article describes approaches to the analysis of publication activity in the field of biomarkers and personalized medicine based on the data from the Web of Science. Objective: The aim of this study is a bibliometric and semantic analysis of the investigation field related to the application of biomarkers for the purposes of personalized medicine. Methods: The evaluation of a number of publications and its’ citations was carried out. The key terms extracted from the most-cited articles were divided into thematic groups. The number of citations of the most popular articles since 2011 was estimated. Results: The citation histories of the top ten articles were considered. Analysis of key terms from different parts of the most-cited articles included statistics and thematic ranking. The comparison of key terms from the most-cited article and the citing articles allowed us to show that the key terminology of the cited article extends to the citing articles. We presented the key terms of the most-cited articles as a terminological map. Conclusion: The study of citation of the articles in the field of personalized medicine and biomarkers was based on a survey on the Web of Science. Based on the analysis of a number of citations the trends and citation histories were constructed. The statistical and thematic analysis of the use of keywords in different sections of articles was done. We have shown that the citing articles spread the key terms of the cited article to identify trends in knowledge development which could be presented as a terminological map. Others: We presented the results in the form of a terminological map of the latest developments in the field of biomarkers in personalized medicine based on proposed principles.

This paper reveals the research hotspots and development directions of case-based reasoning in the field of health care, and proposes the framework and key technologies of medical knowledge service systems based on case-based reasoning (CBR) in the big data environment. The 2124 articles on medical CBR in the Web of Science were visualized and analyzed using a bibliometrics method, and a CBR-based knowledge service system framework was constructed in the medical Internet of all people, things and data resources environment. An intelligent construction method for the clinical medical case base and the gray case knowledge reasoning model were proposed. A cloud-edge collaboration knowledge service system was developed and applied in a pilot project. Compared with other diagnostic tools, the system provides case-based explanations for its predicted results, making it easier for physicians to understand and accept, so that they can make better decisions. The results show that the system has good interpretability, has better acceptance than the common intelligent decision support system, and strongly supports physician auxiliary diagnosis and treatment as well as clinical teaching.

The paper focuses on discovering new knowledge for creating and updating terminological profiles of diseases. A profile is understood as the complex of related annotations of terms describing a disease and its stages. An annotation of each term contains a structured definition of the term meaning (=concept) in which its sub-meanings can be defined, term synonyms, inter-concept relationships, links to external information resources, term contexts extracted from scientific texts on medicine and a linkage between each context and a relevant text, and term associations with disease stages. The growth of scientific knowledge in medicine results in new terms that need to be regularly added to disease profiles. Creating and updating disease profiles requires an advanced model as a theoretical basis for developing a knowledge base and information technology that support the discovery of new knowledge in large collections. The proposed model combines the automatic and expert stages of finding new meanings of existing terms and new terms representing new knowledge concepts that are not described in medical dictionaries and handbooks used by experts. The paper aims to compare our information-technology-oriented model with the spiral model of knowledge creation.