Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding Model: Mixed Method Study and Cluster Analysis.

BACKGROUND: Allergic rhinitis (AR) is a chronic disease, and several risk factors predispose individuals to the condition in their daily lives, including exposure to allergens and inhalation irritants. Analyzing the potential risk factors that can trigger AR can provide reference material for individuals to use to reduce its occurrence in their daily lives. Nowadays, social media is a part of daily life, with an increasing number of people using at least 1 platform regularly. Social media enables users to share experiences among large groups of people who share the same interests and experience the same afflictions. Notably, these channels promote the ability to share health information. OBJECTIVE: This study aims to construct an intelligent method (TopicS-ClusterREV) for identifying the risk factors of AR based on these social media comments. The main questions were as follows: How many comments contained AR risk factor information? How many categories can these risk factors be summarized into? How do these risk factors trigger AR? METHODS: This study crawled all the data from May 2012 to May 2022 under the topic of allergic rhinitis on Zhihu, obtaining a total of 9628 posts and 33,747 comments. We improved the Skip-gram model to train topic-enhanced word vector representations (TopicS) and then vectorized annotated text items for training the risk factor classifier. Furthermore, cluster analysis enabled a closer look into the opinions expressed in the category, namely gaining insight into how risk factors trigger AR. RESULTS: Our classifier identified more comments containing risk factors than the other classification models, with an accuracy rate of 96.1% and a recall rate of 96.3%. In general, we clustered texts containing risk factors into 28 categories, with season, region, and mites being the most common risk factors. We gained insight into the risk factors expressed in each category; for example, seasonal changes and increased temperature differences between day and night can disrupt the body's immune system and lead to the development of allergies. CONCLUSIONS: Our approach can handle the amount of data and extract risk factors effectively. Moreover, the summary of risk factors can serve as a reference for individuals to reduce AR in their daily lives. The experimental data also provide a potential pathway that triggers AR. This finding can guide the development of management plans and interventions for AR.

Internet-Based Healthcare Knowledge Service for Improvement of Chinese Medicine Healthcare Service Quality.

With the development of new-generation information technology and increasing health needs, the requirements for Chinese medicine (CM) services have shifted toward the 5P medical mode, which emphasizes preventive, predictive, personalized, participatory, and precision medicine. This implies that CM knowledge services need to be smarter and more sophisticated. This study adopted a bibliometric approach to investigate the current state of development of CM knowledge services, and points out that accurate knowledge service is an inevitable requirement for the modernization of CM. We summarized the concept of smart CM knowledge services and highlighted its main features, including medical homogeneity, knowledge service intelligence, integration of education and research, and precision medicine. Additionally, we explored the intelligent service method of traditional Chinese medicine under the 5P medical mode to support CM automatic knowledge organization and safe sharing, human-machine collaborative knowledge discovery and personalized dynamic knowledge recommendation. Finally, we summarized the innovative modes of CM knowledge services. Our research will guide the quality assurance and innovative development of the traditional Chinese medicine knowledge service model in the era of digital intelligence.

Internet Healthcare Policy Analysis, Evaluation, and Improvement Path: Multidimensional Perspectives.

Internet healthcare is a crucial component of the healthcare industry's digital transformation and plays a vital role in achieving China's Healthy China strategy and promoting universal health. To ensure the development of internet healthcare is guided by scientifically sound policies, this study analyzes and assesses current policy texts, aiming to identify potential issues and inadequacies. By examining 134 national-level policy documents, utilizing multiple research methods, including policy bibliometrics, content analysis, and the PMC Index Model, the study investigates policy characteristics, distribution of policy instruments, and evaluation outcomes related to internet healthcare. The study findings reveal that internet healthcare policies place emphasis on enhancing service quality, driving technological innovation, and promoting management standardization. Although policy instruments align with the current stage of internet healthcare development in China, they are plagued by imbalances in implementation. While policies are generally well-formulated, there are discernible discrepancies among them, necessitating the reinforcement and refinement of certain provisions. Hence, it is imperative to strategically optimize the amalgamation and implementation of policy instruments while concurrently endeavoring to achieve a dynamic equilibrium in policy combinations. Furthermore, policymakers should diligently refine the policy content pertaining to its nature and effectiveness in order to fully maximize policy utility.

Scientometric analysis of trends in global research on acne treatment.

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.

How Do COVID-19 Risk, Life-Safety Risk, Job Insecurity, and Work-Family Conflict Affect Miner Performance? Health-Anxiety and Job-Anxiety Perspectives.

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.

What Causes Health Information Avoidance Behavior under Normalized COVID-19 Pandemic? A Research from Fuzzy Set Qualitative Comparative Analysis.

Affected by the normalization of the COVID-19 pandemic, people's lives are subject to many restrictions, and they are under enormous psychological and physical pressure. In this situation, health information may be a burden and cause of anxiety for people; thus, the refusal of health information occurs frequently. Health-information-avoidance behavior has produced potential impacts and harms on people's lives. Based on more than 120,000 words of textual data obtained from semi-structured interviews, summarizing a case collection of 55 events, this paper explores the factors and how they combine to lead to avoidance of health information. First, the influencing factors are constructed according to the existing research, and then the fuzzy set qualitative comparative analysis (fsQCA) method is used to discover the configuration relationship of health-information-avoidance behavior. The results show that the occurrence of health-information avoidance is not the result of a single factor but the result of a configuration of health-information literacy, negative emotions, perceived information, health-information presentation, cross-platform distribution, and the network information environment. These findings provide inspiration for reducing the adverse consequences of avoiding health information and improving the construction of health-information service systems.

Trust-Based Research: Influencing Factors of Patients' Medical Choice Behavior in the Online Medical Community.

The medical service is a special credit commodity, and trust plays a very important role in patients' online medical choice behavior. By collecting information about the doctors on China's leading online medical platform (Platform A), a regression analysis model was constructed, based on the credibility theory model, which has the following three dimensions: ability trust, benevolence trust, and integrity trust. The results showed that the medical title of the doctors, their department's reputation, the number of gifts given to them, and the number of patients who registered with them after diagnosis, among other factors, had a significant, positive impact on the behavior of choosing doctors. Among these considerations, the number of patients registered after diagnosis had the greatest impact on the behavior of choosing doctors. This factor is the result of each doctor's personal brand management, which reflects their comprehensive ability, reputation and integrity. Compared with previous studies, this paper creatively analyzed the important influence of departmental reputation and the number of patients registered after diagnosis on medical choice behavior and puts forward that a doctor can use the number of patients registered after diagnosis to manage their personal brand. Based on the results of this study, we will also put forward suggestions from the perspectives of patients, doctors and the online medical community.

An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios.

Increasingly, machine learning methods have been applied to aid in diagnosis with good results. However, some complex models can confuse physicians because they are difficult to understand, while data differences across diagnostic tasks and institutions can cause model performance fluctuations. To address this challenge, we combined the Deep Ensemble Model (DEM) and tree-structured Parzen Estimator (TPE) and proposed an adaptive deep ensemble learning method (TPE-DEM) for dynamic evolving diagnostic task scenarios. Different from previous research that focuses on achieving better performance with a fixed structure model, our proposed model uses TPE to efficiently aggregate simple models more easily understood by physicians and require less training data. In addition, our proposed model can choose the optimal number of layers for the model and the type and number of basic learners to achieve the best performance in different diagnostic task scenarios based on the data distribution and characteristics of the current diagnostic task. We tested our model on one dataset constructed with a partner hospital and five UCI public datasets with different characteristics and volumes based on various diagnostic tasks. Our performance evaluation results show that our proposed model outperforms other baseline models on different datasets. Our study provides a novel approach for simple and understandable machine learning models in tasks with variable datasets and feature sets, and the findings have important implications for the application of machine learning models in computer-aided diagnosis.

A Personalized Medical Decision Support System Based on Explainable Machine Learning Algorithms and ECC Features: Data from the Real World.

Artificial intelligence can help physicians improve the accuracy of breast cancer diagnosis. However, the effectiveness of AI applications is limited by doctors' adoption of the results recommended by the personalized medical decision support system. Our primary purpose is to study the impact of external case characteristics (ECC) on the effectiveness of the personalized medical decision support system for breast cancer assisted diagnosis (PMDSS-BCAD) in making accurate recommendations. Therefore, we designed a novel comprehensive framework for case-based reasoning (CBR) that takes the impact of external features of cases into account, made use of the naive Bayes and k-nearest neighbor (KNN) algorithms (CBR-ECC), and developed a PMDSS-BCAD system by using the CBR-ECC model and external features as system components. Under the new case-based reasoning framework, the accuracy of the combined model of naive Bayes and KNN with an optimal K value of 2 is 99.40%. Moreover, in a real hospital scenario, users rated the PMDSS-BCAD system, which takes into account the external characteristics of the case, better than the original personalized system. These results suggest that PMDSS-BCD can not only provide doctors with more personalized and accurate results for auxiliary diagnosis, but also improve doctors' trust in the results, so as to encourage doctors to adopt the results recommended by the personalized system.

Different Roles of Telehealth and Telemedicine on Medical Tourism: An Empirical Study from Azerbaijan.

With the rapid progress in mobile healthcare and Internet medicine, the impact of telehealth and telemedicine on the satisfaction of patients and their willingness to travel has become a focus of the academic research community. This study analyses the differences between telehealth and telemedicine and their role in medical tourism. We examine how the information quality and communication quality of telehealth and telemedicine influence patient satisfaction, and their effects on patients' willingness to undertake medical travel and on their medical travel behaviours. We conducted an empirical study on the use of telehealth and telemedicine and on medical travel behaviour in Azerbaijan using a survey for data collection. A total of 500 results were collected and analysed using SmartPLS 3.0. Results show that (1) the communication quality and information quality of telehealth and telemedicine and their effects on satisfaction have significantly positive influences on willingness to undertake medical travel; (2) the psychological expectations of value and cost (perceived value and perceived cost) have a positive influence on medical travel; and (3) willingness to participate in medical travel positively influences medical travel behaviour. Moreover, results of this study have implications for research on, and the practice of, using telehealth and telemedicine as they relate to medical tourism. This research may help improve knowledge about telehealth and telemedicine and understand the differences between them in detail. This empirical research model may also be useful for researchers from other countries who wish to measure medical travel behaviour.

Hospital Climate and Peer Report Intention on Adverse Medical Events: Role of Attribution and Rewards.

Adverse medical events (AMEs) often occur in the healthcare workplace, and studies have shown that a positive atmosphere can reduce their incidence by increasing peer report intention. However, few studies have investigated the effect and action mechanism therein. We aimed to extend upon these studies by probing into the relationship between hospital climate and peer report intention, along with the mediating effect of attribution tendency and moderating effects of rewards. For this purpose, a cross-sectional survey was administered in a hospital among health professionals. We collected 503 valid questionnaires from health professionals in China and verified the hypothesis after sorting the questionnaires. The results of empirical analysis show that a positive hospital climate significantly induces individual internal attribution tendency, which in turn exerts a positive effect on peer report intention. Contract reward also helps to increase peer report intention, especially for health professionals with an internal attribution tendency. The findings contribute to the literature regarding AME management in hospitals by providing empirical evidence of the necessity for hospital climate and contract reward, and by providing insights to improve their integrated application.

How Social Media Use at Work Affects Improvement of Older People's Willingness to Delay Retirement During Transfer From Demographic Bonus to Health Bonus: Causal Relationship Empirical Study.

BACKGROUND: With the increased older population in China and the subsequent reduced labor force, the "demographic bonus" is disappearing. The Chinese government proposed a Healthy China strategy in 2017. The transfer of the demographic bonus to a "health bonus" extended the working life of people and reduced the negative impact of the population's aging on the labor force structure. OBJECTIVE: This research focuses on the effect of older workers' social media usage at work on their work ability (related to both physical and mental health) and thus their willingness to delay retirement. METHODS: The questionnaire respondents were older than 55 years, and they obtained the questionnaire from social media, from June to July 2018. A total of 1020 valid questionnaires were collected, and SmartPLS 3.28 (SmartPLS GmbH) was used to analyze the data. Effects were analyzed using 2-tailed t tests. RESULTS: (1) Use of social media at work can improve information support (t14=13.318, P<.001), emotional support (t14=13.184, P<.001), and self-efficacy (t14=6.364, P<.001) for older people; (2) information support is the main factor affecting the self-efficacy of older workers (t14=23.304, P<.001), as compared with emotional support (t14=1.799, P=0.07); (3) the impacts of emotional support on work ability (t14=8.876, P<.001) and work stress (t14=9.545, P<.001) are generally higher than those of information support (t14=4.394, P<.001; t14=5.002, P<.001); (4) self-efficacy has an impact on work ability (t14=5.658, P<.001) and work stress (t14=4.717, P<.001); and (5) the impacts of work ability (t14=8.586, P<.001) and work stress (t14=8.579, P<.001) on retirement willingness are greater than those of emotional support (t14=2.112, P=.04) and information support (t14=4.314, P<.001). CONCLUSIONS: Our study confirms that the use of social media at work has a positive impact on older workers. Based on the findings, we have put forward proposals to extend people's working lives and help governments implement health bonus policies. In the future, we will compare the different values of willingness to delay retirement among older people in different occupations and different cultures.

Mobile Social Media Use and Trailing Parents' Life Satisfaction: Social Capital and Social Integration Perspective.

In recent years, a special group of elderly migrants emerged in some developing countries, namely "trailing parents," who migrated with adult children in their later years. Lots of developing countries such as China have an increasing number of old people using mobile social media for social interaction, information, and entertainment. Based on social capital theory, we developed a research model and investigated the relationship between mobile social media use (MSMU) and social capital, social integration, and life satisfaction of trailing parents. We completed an empirical study by collecting 244 valid samples. The results show that MSMU has a positive impact on trailing parents' life satisfaction, and bonding social capital, bridging social capital and social integration are all important predictors of trailing parents' life satisfaction. Furthermore, social capital and social integration play an important chain mediation role in the relationship between MSMU and trailing parents' life satisfaction.

A Smart Healthcare Knowledge Service Framework for Hierarchical Medical Treatment System.

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.

Visual Analysis on Information Theory and Science of Complexity Approaches in Healthcare Research.

In order to explore the knowledge base, research hotspot, development status, and future research direction of healthcare research based on information theory and complex science, a total of 3031 literature data samples from the core collection of Web of Science from 2003 to 2019 were selected for bibliometric analysis. HistCite, CiteSpace, Excel, and other analytical tools were used to deeply analyze and visualize the temporal distribution, spatial distribution, knowledge evolution, literature co-citation, and research hotspots of this field. This paper reveals the current development of healthcare research field based on information theory and science of complexity, analyzes and discusses the research hotspots and future development that trends in this field, and provides important knowledge support for researchers in this field for further relevant research.

Exploring emerging IoT technologies in smart health research: a knowledge graph analysis.

BACKGROUND: At present, Internet of Things technology has been widely used in various fields, and smart health is also one of its important application areas. METHODS: We use the core collection of Web of Science as a data source, using tools such as CiteSpace and bibliometric methods to visually analyze 9561 articles published in the field of smart health research based on the Internet of things (IoT) in 2003-2019, including time distribution, spatial distribution, and literature co-citation analysis and keyword analysis. RESULTS: The field of smart health research based on IoT has developed rapidly since 2014, but has not yet formed a stable network of authors and institutions. In addition, the knowledge base in this field has been initially formed, and most of the published literatures are multi-theme research. CONCLUSIONS: This study discusses the research status, research hotspots and future development trends in this field, and provides important knowledge support for subsequent research.

Effects of Attitude, Motivation, and Eagerness for Physical Activity among Middle-Aged and Older Adults.

Background: Although physical activity (PA) is a noninvasive and cost-effective method of improving the quality of health, global statistics show that only a few middle-aged and older adults engage in the recommended PAs. This is due to a lack of motivation and companionship. Objective: This study analyses the attitudes and self-determined motivation of Chinese middle-aged and older adults for PAs and their eagerness to participate in PAs such as sports, exercise, and recreational and cultural activities (RCAs), from attitudinal, eagerness, and motivational objectives of PAs perspective. Methods: A cross-sectional study was carried out on 840 middle-aged (35-54 years) and older adults (55+ years). To determine their attitude, eagerness, and self-determined motivation for PA, we used attitudinal, Eagerness for Physical Activity Scale (EPAS), and Situational Motivational Scale (SIMS). The data were analyzed with SPSS 23.0. Results: The results show that 39.1% of the participants were not satisfied with PAs. Compared with females, males reported a less positive attitude towards PAs. Moreover, a positive attitude decreases with age. Participants' motivation and eagerness in activities such as RCAs, exercise, and sports are decreasing. Regarding self-determined motivation, there are gender differences in RCAs, but there is none for exercise and sports participation. Conclusion: The findings show the importance of RCAs and the support of family and friends enhancing the eagerness, attitude, and motivation to participate in PAs. Furthermore, the findings can help to create more effective PA programs for middle-aged and older adults. By engaging in RCAs, participants can reap the benefits of PAs. Participating in RCAs can lead to social equity in health.

A case-based ensemble learning system for explainable breast cancer recurrence prediction.

Significant progress has been achieved in recent years in the application of artificial intelligence (AI) for medical decision support. However, many AI-based systems often only provide a final prediction to the doctor without an explanation of its underlying decision-making process. In scenarios concerning deadly diseases, such as breast cancer, a doctor adopting an auxiliary prediction is taking big risks, as a bad decision can have very harmful consequences for the patient. We propose an auxiliary decision support system that combines ensemble learning with case-based reasoning to help doctors improve the accuracy of breast cancer recurrence prediction. The system provides a case-based interpretation of its prediction, which is easier for doctors to understand, helping them assess the reliability of the system's prediction and make their decisions accordingly. Our application and evaluation in a case study focusing on breast cancer recurrence prediction shows that the proposed system not only provides reasonably accurate predictions but is also well-received by oncologists.

How transformational leadership and clan culture influence nursing staff's willingness to stay.

AIM: To explore the impact mechanism of transformational leadership and clan culture on the willingness to stay of nursing staff in the aged care industry. BACKGROUND: Previous researchers had paid much attention to the leadership and culture, yet it was unclear how the transformational leadership and clan culture influence the willingness to stay of nursing staff, especially those in the aged care industry. METHODS: A questionnaire was conducted in an aged care agency in Anhui Province, with a total of 217 valid questionnaires collected for empirical analysis. RESULTS: (a) Transformational leadership indirectly affects willingness to stay by affecting organisational commitment and job satisfaction. (b) Clan culture also affects the willingness to stay by affecting their organisational commitment and job satisfaction. (c) Professional identity also has a significant positive impact on the willingness to stay. CONCLUSIONS: The findings of the study emphasize the importance of transformational leadership and clan culture in enhancing the willingness to stay of nursing staff in the aged care industry, and clarify the specific impact mechanism. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse leaders in the aged care agencies should improve transformational leadership, and strive to build clan culture to help retain nursing staff better.

Factors Influencing College Students' Mental Health Promotion: The Mediating Effect of Online Mental Health Information Seeking.

For college students, mental health is an important factor in ensuring their ability to study and have a normal life. This research focuses on factors affecting the mental health of college students in the information network society. We constructed a theoretical model that influences their online mental health information seeking behavior from internal and external perspectives, and by extension, affects their mental health. Through the data obtained by field research and questionnaire survey on the online mental health information seeking behavior of some college students in Internet health information platforms, a structural equation model is used to test the hypotheses. Results show that the quality of external Internet platforms and the quality of internal electronic health literacy have a significantly positive impact on the online health information searching behavior of college students; electronic health literacy and online mental health information seeking behavior have significantly direct positive effects on college students' mental health. Further, online health information searching behavior has a significant mediating effect between Internet platform quality, electronic health literacy, and college students' mental health. The research conclusions have theoretical value and practical significance to study the factors influencing college students' mental health in the context of information network society.