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Due to the increasing cost of health insurance, for decades, many countries have endeavored to constrain the cost of insurance by utilizing a DRG payment system. In most cases, under the DRG payment system, hospitals cannot exactly know which DRG code inpatients are until they are discharged. This paper focuses on the prediction of what DRG code appendectomy patients will be classified with when they are admitted to hospital. We utilize two models (or classifiers) constructed using the C4.5 algorithm and back-propagation neural network (BPN). We conducted experiments with the data collected from two hospitals. The results show that the accuracies of these two classification models can be up to 97.84% and 98.70%, respectively. According to the predicted DRG code, hospitals can effectively arrange medical resources with certainty, then, in turn, improve the quality of the medical care patients receive. [ABSTRACT FROM AUTHOR]

Mt. Jade (or "Yushan" in Chinese) is the highest peak in northeast Asia. The topography is very rugged and complicated. Such terrain makes it difficult to obtain the correct results for terrain corrections (TCs). This paper developed an improved approach, named cone-section method, to compute the TCs of the Mt. Jade area using a high-resolution digital elevation model (DEM) on a 9" × 9" grid. The corrections were calculated to the distance of 100 km with an average rock density of 2.57 × 10³ kg·m-3. This investigation compared the results of TCs from the cone-section method with those from the cylinder prism and Gaussian quadrature methods using a 9" × 9" elevation grid for the inner zone and a 90" × 90" elevation grid for the outer zone. The inner and outer radii were set to 20 and 200 km, respectively. The comparisons showed that the cone-section algorithm is consistent with the Gaussian quadrature. Furthermore, the cone-section method is an appropriate approach for TCs in high elevation areas, yielding results that outperform the cylinder prism method. [ABSTRACT FROM AUTHOR]

With the advent of new satellite technology, the radiative energy exchanges between Sun, Earth, and space may now be quantified accurately. Nevertheless, much less is known about the magnitude of the energy flows within the climate system and at the Earth's surface, which cannot be directly measured by satellites. This review surveys the basic theories, observational methods, and different surface energy balance algorithms for estimating evapotranspiration (ET) from landscapes and regions with remotely sensed surface temperatures, and highlights uncertainties and limitations associated with those estimation methods. Although some of these algorithms were built up for specific land covers like irrigation areas only, methods developed for other disciplines like hydrology and meteorology, are also reviewed, where continuous estimates in space and in time are needed. Temporal and spatial scaling issues associated with the use of thermal remote sensing for estimating evapotranspiration are also discussed. A review of these different satellite based remote sensing approaches is presented. The main physical bases and assumptions of these algorithms are also discussed. Some results are shown for the estimation of evapotranspiration on a rice paddy of Chiayi Plain in Taiwan using remote sensing data. [ABSTRACT FROM AUTHOR]

With the unique rainfall patterns of typhoons, plum rains, and short-term heavy rainfalls, the frequent landslide and debris flow disasters have caused severe loss to people in Taiwan. In the studies of landslide susceptibility, the information of factors used for analysis was usually annual-based content, and it was assumed that the same elements from different years were independent between each year. However, the occurrence of landslides was usually not simply due to the changes within a year. Instead, landslides were triggered because the factors that affected the potential of landslides reached critical conditions after a cumulative change with time. Therefore, this study had well evaluated the influence of temporal characteristics and the ratios of antecedent landslide areas in the past five years in the landslide potential evaluation model. The analysis was conducted through the random forest (RF) algorithm. Additional rainfall events of 2017 were used to test the proposed model's performance to understand its practicality. The analysis results show that in the study area, the RF model had considerably acceptable performance. The results have also demonstrated that the antecedent landslide ratios in the past five years were essential to describe the significance of cumulative change with time when conducting potential landslide evaluation. [ABSTRACT FROM AUTHOR]

Multi-sensor data from different satellites are used to identify an upwelling area in the sea off northeast Taiwan. Sea surface temperature (SST) data derived from infrared and microwave, as well as sea surface height anomaly (SSHA) data derived from satellite altimeters are used for this study. An integration filtering algorithm based on SST data is developed for detecting the cold patch induced by the upwelling. The center of the cold patch is identified by the maximum negative deviation relative to the spatial mean of a SST image within the study area and its climatological mean of each pixel. The boundary of the cold patch is found by the largest SST gradient. The along track SSHA data derived from satellite altimeters are then used to verify the detected cold patch. Applying the detecting algorithm, spatial and temporal characteristics and variations of the cold patch are revealed. The cold patch has an average area of 1.92 × 104 km². Its occurrence frequencies are high from June to October and reach a peak in July. The mean SST of the cold patch is 23.8 °C. In addition to the annual and the intraseasonal fluctuation with main peak centered at 60 days, the cold patch also has a variation period of about 4.7 years in the interannual timescale. This implies that the Kuroshio variations and long-term and large scale processes playing roles in modifying the cold patch occurrence frequency. [ABSTRACT FROM AUTHOR]

The National Health Insurance Administration of Taiwan has implemented global budget payments, the Diagnosis-Related Group (DRG) inpatient diagnosis-related group payment system, and the same-disease payment system, in order to decrease the financial burden of medical expenditure. However, the benefit system reduces the income of doctors and hospitals. This study proposed an early warning payment algorithm that applies data analytics technology to diabetes hospitalization- and treatment-related fees. A model was constructed based on the characteristics of the Exponentially Weighted Moving Average (EWMA) algorithm to develop control charts, which were first employed using the 2001–2017 health insurance statistical database released by the Department of Health Insurance (DHI). This model was used to simulate data from inpatients with diabetes, to create an early warning algorithm for diagnosis-related groups' (DRGs') medical payments as well as to measure its accuracy. This study will provide a reference for the formulation of payment policies by the DHI. [ABSTRACT FROM AUTHOR]

This study proposes a shape approximation approach to portray the regions of interest (ROI) from medical imaging data. An effective algorithm to achieve an optimal approximation is proposed based on the framework of Particle Swarm Optimization. The convergence of the proposed algorithm is derived under mild assumptions on the selected family of shape equations. The issue of detecting Parkinson's disease (PD) based on the Tc-99m TRODAT-1 brain SPECT/CT images of 634 subjects, with 305 female and an average age of 68.3 years old from Kaohsiung Chang Gung Memorial Hospital, Taiwan, is employed to demonstrate the proposed procedure by fitting optimal ellipse and cashew-shaped equations in the 2D and 3D spaces, respectively. According to the visual interpretation of 3 experienced board-certified nuclear medicine physicians, 256 subjects are determined to be abnormal, 77 subjects are potentially abnormal, 174 are normal, and 127 are nearly normal. The coefficients of the ellipse and cashew-shaped equations, together with some well-known features of PD existing in the literature, are employed to learn PD classifiers under various machine learning approaches. A repeated hold-out with 100 rounds of 5-fold cross-validation and stratified sampling scheme is adopted to investigate the classification performances of different machine learning methods and different sets of features. The empirical results reveal that our method obtains 0.88 ± 0.04 classification accuracy, 0.87 ± 0.06 sensitivity, and 0.88 ± 0.08 specificity for test data when including the coefficients of the ellipse and cashew-shaped equations. Our findings indicate that more constructive and useful features can be extracted from proper mathematical representations of the 2D and 3D shapes for a specific ROI in medical imaging data, which shows their potential for improving the accuracy of automated PD identification. [ABSTRACT FROM AUTHOR]