Deep learning course development and evaluation of artificial intelligence in vocational senior high schools.

This study aimed to develop cross-domain deep learning courses of artificial intelligence in vocational senior high schools and explore its impact on students' learning effects. It initially adopted a literature review to develop a cross-domain SPOC-AIoT Course with SPOC (small private online courses) and the Double Diamond 4D model in vocational senior high schools. Afterward, it adopted participatory action research (PAR) and a questionnaire survey and conducted analyses on the various aspects of the technology acceptance model by SmartPLS. Further, this study explored the impact on the effects of deep learning and knowledge-ability learning of artificial intelligence after 16 weeks of course teaching among 36 Grade I students from the electrical and electronic group of a vocational senior high school. This study revealed that (1) the four stages of the SPOC-AIoT Teaching Mode of the Double Diamond 4D model may effectively guide students to learn AIoT knowledge and skills. (2) Based on the technology acceptance model, the analysis of learning and participation in SmartPLS indicated that this model conformed to the academic fitness requirements of the overall model. (3) After learning with the SPOC-AIoT Teaching Mode, the learning effects of students in AIoT have been significantly improved to a positive aspect. Finally, some suggestions were put forward to promote the development of the SPOC-AIoT Teaching Mode Course in the future.

Applications of mobile learning on college students' water leisure sports in the post-pandemic era.

This study explored the relationship and influence of college students' participation in water leisure sports, as well as the technology acceptance model (TAM). With the rapid development of the economy, the government is promoting various water leisure sports centered on the concept and policy of a maritime- and ocean-based nation. Based on the TAM, this study investigated the relationships among its ease of use, usefulness, water leisure involvement, benefits, barriers, and intentions to participate in water activities in connection with college students participating in water leisure sports. A total of 420 college students who participated in water leisure activities were sampled. There were 370 valid questionnaires, and the recovery rate of valid questionnaires was 82.2%. The data were analyzed by the structural equation modeling of the partial least squares method (PLS-SEM). The results show that the ease of use of water facilities had a positive effect on the usefulness, involvement, and participation in water activities; the usefulness of water facilities had a positive and significant impact on the intention to participate in water activities; water leisure involvement had a positive and significant impact on the benefits and the intention to participate in water activities; the intention to participate in water activities had a positive and significant impact on the benefits of water leisure activities. Furthermore, the study found that the intention to participate in water activities had a mediating effect between water leisure involvement and water leisure benefits; water leisure involvement had a mediating effect between the ease of use of water facilities and the intention to participate in water activities; the usefulness of water facilities had a mediating effect between the ease of use of water facilities and the intention to participate in water activities. In addition, the interaction between water leisure involvement and water leisure constraints had an interfering effect on water leisure benefits. Accordingly, recommendations for promotion and implementation are provided. Based on the TAM, the study provided suggestions for implementing water leisure sports to promote college students' participation behavior in water leisure sports.

Predicting 30-Day Readmission for Stroke Using Machine Learning Algorithms: A Prospective Cohort Study.

Background: Machine learning algorithms for predicting 30-day stroke readmission are rarely discussed. The aims of this study were to identify significant predictors of 30-day readmission after stroke and to compare prediction accuracy and area under the receiver operating characteristic (AUROC) curve in five models: artificial neural network (ANN), K nearest neighbor (KNN), random forest (RF), support vector machine (SVM), naive Bayes classifier (NBC), and Cox regression (COX) models. Methods: The subjects of this prospective cohort study were 1,476 patients with a history of admission for stroke to one of six hospitals between March, 2014, and September, 2019. A training dataset (n = 1,033) was used for model development, and a testing dataset (n = 443) was used for internal validation. Another 167 patients with stroke recruited from October, to December, 2019, were enrolled in the dataset for external validation. A feature importance analysis was also performed to identify the significance of the selected input variables. Results: For predicting 30-day readmission after stroke, the ANN model had significantly (P < 0.001) higher performance indices compared to the other models. According to the ANN model results, the best predictor of 30-day readmission was PAC followed by nasogastric tube insertion and stroke type (P < 0.05). Using a machine learning ANN model to obtain an accurate estimate of 30-day readmission for stroke and to identify risk factors may improve the precision and efficacy of management for these patients. Conclusion: Using a machine-learning ANN model to obtain an accurate estimate of 30-day readmission for stroke and to identify risk factors may improve the precision and efficacy of management for these patients. For stroke patients who are candidates for PAC rehabilitation, these predictors have practical applications in educating patients in the expected course of recovery and health outcomes.

Cost and Effectiveness of Long-Term Care Following Integrated Discharge Planning: A Prospective Cohort Study.

Little is known about the effects of seamless hospital discharge planning on long-term care (LTC) costs and effectiveness. This study evaluates the cost and effectiveness of the recently implemented policy from hospital to LTC between patients discharged under seamless transition and standard transition. A total of 49 elderly patients in the standard transition cohort and 119 in the seamless transition cohort were recruited from November 2016 to February 2018. Data collected from medical records included the Multimorbidity Frailty Index, Activities of Daily Living Scale, and Malnutrition Universal Screening Tool during hospitalization. Multiple linear regression and Cox regression models were used to explore risk factors for medical resource utilization and medical outcomes. After adjustment for effective predictors, the seamless cohort had lower direct medical costs, a shorter length of stay, a higher survival rate, and a lower unplanned readmission rate compared to the standard cohort. However, only mean total direct medical costs during hospitalization and 6 months after discharge were significantly (p < 0.001) lower in the seamless cohort (USD 6192) compared to the standard cohort (USD 8361). Additionally, the annual per-patient economic burden in the seamless cohort approximated USD 2.9-3.3 billion. Analysis of the economic burden of disability in the elderly population in Taiwan indicates that seamless transition planning can save approximately USD 3 billion in annual healthcare costs. Implementing this policy would achieve continuous improvement in LTC quality and reduce the financial burden of healthcare on the Taiwanese government.

Breast Cancer Surgery 10-Year Survival Prediction by Machine Learning: A Large Prospective Cohort Study.

Machine learning algorithms have proven to be effective for predicting survival after surgery, but their use for predicting 10-year survival after breast cancer surgery has not yet been discussed. This study compares the accuracy of predicting 10-year survival after breast cancer surgery in the following five models: a deep neural network (DNN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classifier (NBC) and Cox regression (COX), and to optimize the weighting of significant predictors. The subjects recruited for this study were breast cancer patients who had received breast cancer surgery (ICD-9 cm 174-174.9) at one of three southern Taiwan medical centers during the 3-year period from June 2007, to June 2010. The registry data for the patients were randomly allocated to three datasets, one for training (n = 824), one for testing (n = 177), and one for validation (n = 177). Prediction performance comparisons revealed that all performance indices for the DNN model were significantly (p < 0.001) higher than in the other forecasting models. Notably, the best predictor of 10-year survival after breast cancer surgery was the preoperative Physical Component Summary score on the SF-36. The next best predictors were the preoperative Mental Component Summary score on the SF-36, postoperative recurrence, and tumor stage. The deep-learning DNN model is the most clinically useful method to predict and to identify risk factors for 10-year survival after breast cancer surgery. Future research should explore designs for two-level or multi-level models that provide information on the contextual effects of the risk factors on breast cancer survival.

Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study.

No studies have discussed machine learning algorithms to predict recurrence within 10 years after breast cancer surgery. This study purposed to compare the accuracy of forecasting models to predict recurrence within 10 years after breast cancer surgery and to identify significant predictors of recurrence. Registry data for breast cancer surgery patients were allocated to a training dataset (n = 798) for model development, a testing dataset (n = 171) for internal validation, and a validating dataset (n = 171) for external validation. Global sensitivity analysis was then performed to evaluate the significance of the selected predictors. Demographic characteristics, clinical characteristics, quality of care, and preoperative quality of life were significantly associated with recurrence within 10 years after breast cancer surgery (p < 0.05). Artificial neural networks had the highest prediction performance indices. Additionally, the surgeon volume was the best predictor of recurrence within 10 years after breast cancer surgery, followed by hospital volume and tumor stage. Accurate recurrence within 10 years prediction by machine learning algorithms may improve precision in managing patients after breast cancer surgery and improve understanding of risk factors for recurrence within 10 years after breast cancer surgery.