Time-Aware Missing Healthcare Data Prediction Based on ARIMA Model.

Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose meters, electrocardiographs), which results in the generation of large amounts of data. Healthcare data is essential in patient management and plays a critical role in transforming healthcare services, medical scheme design, and scientific research. Missing data is a challenging problem in healthcare due to system failure and untimely filing, resulting in inaccurate diagnosis treatment anomalies. Therefore, there is a need to accurately predict and impute missing data as only complete data could provide a scientific and comprehensive basis for patients, doctors, and researchers. However, traditional approaches in this paradigm often neglect the effect of the time factor on forecasting results. This paper proposes a time-aware missing healthcare data prediction approach based on the autoregressive integrated moving average (ARIMA) model. We combine a truncated singular value decomposition (SVD) with the ARIMA model to improve the prediction efficiency of the ARIMA model and remove data redundancy and noise. Through the improved ARIMA model, our proposed approach (named MHDP SVD_ARIMA) can capture underlying pattern of healthcare data changes with time and accurately predict missing data. The experiments conducted on the WISDM dataset show that MHDP SVD_ARIMA approach is effective and efficient in predicting missing healthcare data.

Social Media Improves Students' Academic Performance: Exploring the Role of Social Media Adoption in the Open Learning Environment among International Medical Students in China.

Numerous studies have examined the role of social media as an open-learning (OL) tool in the field of education, but the empirical evidence necessary to validate such OL tools is scant, specifically in terms of student academic performance (AP). In today's digital age, social media platforms are most popular among the student community, and they provide opportunities for OL where they can easily communicate, interact, and collaborate with each other. The authors of this study aimed to minimize the literature gap among student communities who adopt social media for OL, which has positive impacts on their AP in Chinese higher education. We adopted social constructivism theory (SCT) and the technology acceptance model (TAM) to formulate a conceptual framework. Primary data containing 233 questionnaires of international medical students in China were collected in January 2021 through the survey method. The gathered data were analyzed through structural equation modeling techniques with SmartPLS 3. The results revealed that perceived usefulness, perceived ease of use, and interactions with peers have positive and significant influence on OL. In addition, OL was found to have positive and significant influence on students' AP and engagement. Lastly, engagement showed a positive impact on students' AP. Thus, this study shows that social media serves as a dynamic tool to expedite the development of OL settings by encouraging collaboration, group discussion, and the exchange of ideas between students that reinforce their learning behavior and performance.