Differentiation between Crohn’s disease and intestinal tuberculosis is difficult but crucial for medical decisions. This study aims to develop an effective framework to distinguish these two diseases through an explainable machine learning (ML) model. After feature selection, a total of nine variables are extracted, including intestinal surgery, abdominal, bloody stool, PPD, knot, ESAT-6, CFP-10, intestinal dilatation and comb sign. Besides, we compared the predictive performance of the ML methods with traditional statistical methods. This work also provides insights into the ML model’s outcome through the SHAP method for the first time. A cohort consisting of 200 patients’ data (CD = 160, ITB = 40) is used in training and validating models. Results illustrate that the XGBoost algorithm outperforms other classifiers in terms of area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision and Matthews correlation coefficient (MCC), yielding values of 0.891, 0.813, 0.969, 0.867 and 0.801 respectively. More importantly, the prediction outcomes of XGBoost can be effectively explained through the SHAP method. The proposed framework proves that the effectiveness of distinguishing CD from ITB through interpretable machine learning, which can obtain a global explanation but also an explanation for individual patients.

The COVID-19 pandemic has created unprecedented burdens on people’s health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of public discussion and sentiment evolution of the vaccine, particularly the differences in topics of concern between vaccine-support and vaccine-hesitant groups, remains scarce. Using social media data from the two years following the outbreak of COVID-19 (23 January 2020 to 23 January 2022), coupled with state-of-the-art natural language processing (NLP) techniques, we developed a public opinion analysis framework (BertFDA). First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. Second, by building an improved Bert pre-training model for sentiment classification, we provide evidence that public negative sentiment continued to decline in the early stages of COVID-19 vaccination. Third, by modeling and analyzing the microblog posts from the vaccine-support group and the vaccine-hesitant group, we discover that the vaccine-support group was more concerned about vaccine effectiveness and the reporting of news, reflecting greater group cohesion, whereas the vaccine-hesitant group was particularly concerned about the spread of coronavirus variants and vaccine side effects. Finally, we deployed different machine learning models to predict public opinion. Moreover, functional data analysis (FDA) is developed to build the functional sentiment curve, which can effectively capture the dynamic changes with the explicit function. This study can aid governments in developing effective interventions and education campaigns to boost vaccination rates.

As people pay ever-increasing attention to the problems caused by psychological stress, research on its influencing factors becomes crucial. This study analyzed the Health Information National Trends Survey (HINTS, Cycle 3 and Cycle 4) data (N = 5484) and assessed the outcomes using descriptive statistics, Chi-squared tests, and t-tests. Four machine learning algorithms were applied for modeling: logistic regression (linear), random forests (RF) (ensemble), the artificial neural network (ANN) (nonlinear), and gradient boosting (GB) (ensemble). The samples were randomly assigned to a 50% training set and a 50% validation set. Twenty-six preselected variables from the databases were used in the study as predictors, and the four models identified twenty predictors of psychological distress. The essence of this paper is a binary classification problem of judging whether an individual has psychological distress based on many different factors. Therefore, accuracy, precision, recall, F1-score, and AUC were used to evaluate the model performance. The logistic regression model selected predictors by forward selection, backward selection, and stepwise regression; variable importance values were used to identify predictors in the other three machine learning methods. Of the four machine learning models, the ANN exhibited the best predictive effect (AUC = 73.90%). A range of predictors of psychological distress was identified by combining the four machine learning models, which would help improve the performance of the existing mental health screening tools.

Organizations have long been actively seeking ways to reduce unethical behavior among employees. However, employee time theft is widespread and costly across various industries, and related research remains relatively limited. Therefore, this study employed social exchange theory to empirically investigate how and when work connectivity behavior promotes employee time theft. Drawing on a sample of 330 employees, our findings indicate that work connectivity behavior positively impacts employee time theft by triggering revenge motives among employees. Furthermore, it was discovered that leader–member exchange weakens both the direct effect of work connectivity behavior on revenge motive and the indirect effect of work connectivity behavior on employee time theft via revenge motive. This research developed and elucidated a moderated mediation model, providing valuable insights for both theory and practice.

China announced the Healthy China Initiative (2019–2030) in 2019, an action program aimed to support the country’s current long-term health policy, Healthy China 2030, which focuses on public health promotion and health awareness. Following the implementation of the policy, China had the COVID-19 pandemic, which had an influence on both the public’s degree of health awareness and the adoption of the HCI. This research examines whether the COVID-19 epidemic has increased public understanding and acceptance of China’s long-term health policy. In addition, it analyzes whether the Chinese public’s awareness of health policy has been impacted by China’s usage of smart healthcare in its response to the pandemic. To correspond to these study aims, we used a questionnaire based on the research questions and recent relevant research. The results of the study, based on an examination of 2488 data, demonstrate that the Healthy China Initiative is still poorly understood. More than 70% of respondents were unfamiliar with it. However, the results imply that respondents are becoming more aware of smart healthcare and that public acceptance of official health policies can be aided by the sharing of knowledge about this. As a result, we examine the situation and draw the conclusion that the spread of cutting-edge health-related technology can enhance the communication of health policy and provide participants and policymakers with fresh insights. Finally, this study also can provide lessons for other countries in the early stages of policy dissemination, particularly health policy advocacy and promotion during epidemics.