In the past few decades, international tourism has grown rapidly and has become a very interesting topic in tourism research. Taiwan, acting as a citizen in the global community, improved traveling facilities, and governments' strong promotion has drawn more and more visitors to visit Taiwan. This study tries to build the forecasting model of visitors to Taiwan using three commonly adopted ARIMA, artificial neural networks (ANNs), and multivariate adaptive regression splines (MARS). In order to evaluate the appropriateness of the proposed modeling approaches, the dataset of monthly visitors to Taiwan was used as the illustrative example. Analytic results demonstrated that ARIMA outperformed ANNs and MARS approaches in terms of RMSE, MAD, and MAPE and provided effective alternatives for forecasting tourism demand.

In "classic" biomedical research, diseases have usually been studied individually. The pioneering human disease network (HDN) studies jointly consider a large number of diseases, analyse their interconnections, and provide a more comprehensive description of diseases. However, most of the existing HDN studies are based on molecular information and can only partially describe disease interconnections. Building on the unique Taiwan National Health Insurance Research Database (NHIRD), in this study, we construct the epidemiological HDN (eHDN), where two diseases are concluded as interconnected if their observed probability of co-occurrence deviating that expected under independence. Advancing from the existing HDN, the eHDN can also accommodate nonmolecular connections and have more important practical implications. Building on the network construction, we examine important network properties such as connectivity, module, hub, and others and describe their temporal patterns. This study is among the first to systematically construct the eHDN and can have important implications for human disease research and health care and management.

Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults’ survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study’s advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients’ survival risk before a CABG operation, early prevention and disease management would be possible.

Artificial neural network is a better modeling technique and the overall predictive accuracy is higher on the basis of multiple variables related to laboratory tests. LMGB, high preoperative triglyceride level, and low HbAlc level can predict successful weight reduction at 2 years.

District hospitals belonging to low-throughput and low-performance groups were encouraged to improve resource utilization for enhancing health care service efficiency.