Association of clinical symptomatic hypoglycemia with cardiovascular events and total mortality in type 2 diabetes a nationwide population-based study

OBJECTIVE Hypoglycemia is associated with serious health outcomes for patients treated for diabetes. However, the outcome of outpatients with type 2 diabetes who have experienced hypoglycemia episodes is largely unknown. RESEARCH DESIGN AND METHODS The study population, derived from the National Health Insurance Research Database released by the Taiwan National Health Research Institutes during 1998–2009, comprised 77,611 patients with newly diagnosed type 2 diabetes. We designed a prospective study consisting of randomly selected hypoglycemic type 2 diabetic patients and matched type 2 diabetic patients without hypoglycemia. We investigated the relationships of hypoglycemia with total mortality and cardiovascular events, including stroke, coronary heart disease, cardiovascular diseases, and all-cause hospitalization. RESULTS There were 1,844 hypoglycemic events (500 inpatients and 1,344 outpatients) among the 77,611 patients. Both mild (outpatient) and severe (inpatient) hypoglycemia cases had a higher percentage of comorbidities, including hypertension, renal diseases, cancer, stroke, and heart disease. In multivariate Cox regression models, including diabetes treatment adjustment, diabetic patients with hypoglycemia had a significantly higher risk of cardiovascular events during clinical treatment periods. After constructing a model adjusted with propensity scores, mild and severe hypoglycemia still demonstrated higher hazard ratios (HRs) for cardiovascular diseases (HR 2.09 [95% CI 1.63–2.67]), all-cause hospitalization (2.51 [2.00–3.16]), and total mortality (2.48 [1.41–4.38]). CONCLUSIONS Symptomatic hypoglycemia, whether clinically mild or severe, is associated with an increased risk of cardiovascular events, all-cause hospitalization, and all-cause mortality. More attention may be needed for diabetic patients with hypoglycemic episodes.Pai-Feng Hsu, Shih-Hsien Sung, Hao-Min Cheng, Jong-Shiuan Yeh, Wen-Ling Liu, Wan-Leong Chan, Chen-Huan Chen, Pesus Chou, Shao-Yuan Chuan

The Comparison of Density-Based Clustering Approach among Different Machine Learning Models on Paddy Rice Image Classification of Multispectral and Hyperspectral Image Data

The analysis, measurement, and computation of remote sensing images often require enhanced unsupervised/supervised classification approaches. The goal of this study is to have a better understanding of (a) the classification performance of multispectral image and hyperspectral image data; (b) the classification performance of unsupervised and supervised models; and (c) the classification performance of feature selection among different models. More specifically, the multispectral images and hyperspectral images with high spatial resolution are well accepted for improving land use and classification. Hence, this study used multispectral images (WorldView-2) and hyperspectral images (CASI-1500) and focused on the classifiers K-means, density-based spatial clustering of applications with noise (DBSCAN), linear discriminant analysis (LDA), and back-propagation neural network (BPN). Then the feature selection (principle component analysis, PCA) on four classifiers is studied. The results show that the image material of CASI-1500 classification accuracy is slightly better than that of WorldView-2. The overall classification of BPN is the best, the overall data has a κ value of 0.89 and the overall accuracy is 97%. The DBSCAN presents a reality with good accuracy and the integrity of the thematic map. The DBSCAN can attain an accuracy of about 88% and save 95.1% of computational time

Discrete rough set analysis of two different soil-behavior-induced landslides in National Shei-Pa Park, Taiwan

The governing factors that influence landslide occurrences are complicated by the different soil conditions at various sites. To resolve the problem, this study focused on spatial information technology to collect data and information on geology. GIS, remote sensing and digital elevation model (DEM) were used in combination to extract the attribute values of the surface material in the vast study area of Shei-Pa National Park, Taiwan. The factors influencing landslides were collected and quantification values computed. The major soil component of loam and gravel in the Shei-Pa area resulted in different landslide problems. The major factors were successfully extracted from the influencing factors. Finally, the discrete rough set (DRS) classifier was used as a tool to find the threshold of each attribute contributing to landslide occurrence, based upon the knowledge database. This rule-based knowledge database provides an effective and urgent system to manage landslides. NDVI (Normalized Difference Vegetation Index), VI (Vegetation Index), elevation, and distance from the road are the four major influencing factors for landslide occurrence. The landslide hazard potential diagrams (landslide susceptibility maps) were drawn and a rational accuracy rate of landslide was calculated. This study thus offers a systematic solution to the investigation of landslide disasters

A Development of a Robust Machine for Removing Irregular Noise with the Intelligent System of Auto-Encoder for Image Classification of Coastal Waste

Currently, the seashore is threatened by the environment of climate change and increasing coastal waste. The past environmental groups used a large amount of manpower to manage the coast to maintain the seashore environment. The computational time cost and efficiency are not ideal for the vast area of the seashore. With the progress of GIS (Geographic Information System) technology, the ability of remote sensing technology can capture a wide range of data in a short period. This research is based on the application of remote sensing technology combined with machine learning to display the observation of our seashore. However, in the process of image classification, the seashore wastes are small, which required the use of high-resolution image data. Thus, how to remove the noise becomes a crucial issue in developing an image classifier machine. The difficulties include how to adjust the value of parameters for removing/avoiding noises. First, the texture information and vegetation indices were employed as ancillary information in our image classification. On the other hand, auto-encoder is a very good tool to denoise a given image; hence, it is used to transform high-resolution images by considering ancillary information to extract attributes. Multi-layer perceptron (MLP) and support vector machine (SVM) were compared for classifier performance in a parallel study. The overall accuracy is about 85.5% and 83.9% for MLP and SVM, respectively. If the AE is applied for preprocessing, the overall accuracy is increased by about 10–12%

A Development of a Robust Machine for Removing Irregular Noise with the Intelligent System of Auto-Encoder for Image Classification of Coastal Waste

Currently, the seashore is threatened by the environment of climate change and increasing coastal waste. The past environmental groups used a large amount of manpower to manage the coast to maintain the seashore environment. The computational time cost and efficiency are not ideal for the vast area of the seashore. With the progress of GIS (Geographic Information System) technology, the ability of remote sensing technology can capture a wide range of data in a short period. This research is based on the application of remote sensing technology combined with machine learning to display the observation of our seashore. However, in the process of image classification, the seashore wastes are small, which required the use of high-resolution image data. Thus, how to remove the noise becomes a crucial issue in developing an image classifier machine. The difficulties include how to adjust the value of parameters for removing/avoiding noises. First, the texture information and vegetation indices were employed as ancillary information in our image classification. On the other hand, auto-encoder is a very good tool to denoise a given image; hence, it is used to transform high-resolution images by considering ancillary information to extract attributes. Multi-layer perceptron (MLP) and support vector machine (SVM) were compared for classifier performance in a parallel study. The overall accuracy is about 85.5% and 83.9% for MLP and SVM, respectively. If the AE is applied for preprocessing, the overall accuracy is increased by about 10–12%

The Legalisation of Ethical Governance in Taiwan Biobank Development

Asian Bioethics Review3285-9

An Innovative Intelligent System with Integrated CNN and SVM: Considering Various Crops through Hyperspectral Image Data

Generation of a thematic map is important for scientists and agriculture engineers in analyzing different crops in a given field. Remote sensing data are well-accepted for image classification on a vast area of crop investigation. However, most of the research has currently focused on the classification of pixel-based image data for analysis. The study was carried out to develop a multi-category crop hyperspectral image classification system to identify the major crops in the Chiayi Golden Corridor. The hyperspectral image data from CASI (Compact Airborne Spectrographic Imager) were used as the experimental data in this study. A two-stage classification was designed to display the performance of the image classification. More specifically, the study used a multi-class classification by support vector machine (SVM) + convolutional neural network (CNN) for image classification analysis. SVM is a supervised learning model that analyzes data used for classification. CNN is a class of deep neural networks that is applied to analyzing visual imagery. The image classification comparison was made among four crops (paddy rice, potatoes, cabbages, and peanuts), roads, and structures for classification. In the first stage, the support vector machine handled the hyperspectral image classification through pixel-based analysis. Then, the convolution neural network improved the classification of image details through various blocks (cells) of segmentation in the second stage. A series of discussion and analyses of the results are presented. The repair module was also designed to link the usage of CNN and SVM to remove the classification errors

The Robust Study of Deep Learning Recursive Neural Network for Predicting of Turbidity of Water

Water treatment is an important process, as it improves water quality and makes it better for any end use, whether it be drinking, industrial use, irrigation, water recreation, or any other kind of use. Turbidity is one of the fundamental measurements of the clarity of water in water treatment. Specifically, this component is an optical feature of the amount of light on scatter particles when light is shined on a water sample. It is crucial in water reservoirs to provide clean water, which is difficult to manage and predict. Hence, this study focuses on the use of robust deep learning models to analyze time-series data in order to predict the water quality of turbidity in a reservoir area. Deep learning models may become an alternative solution in predicting water quality because of their accuracy. This study is divided into two parts: (a) the first part uses the optical bands of blue (B), green (G), red (R), and infrared (IR) to build a regression function to monitor turbidity in water, and (b) the second part uses a hybrid model to analyze time-series turbidity data with the recursive neural network (RNN2) model. The selected models’ accuracies are compared based on the accuracy using the input data, forecasting level, and training time. The analysis shows that these models have their strengths and limitations under different analyzed conditions. Generally, RNN2 shows the performance regarding the root-mean-square error (RMSE) evaluation metric. The most significant finding is that the RNN2 model is suitable for the accurate prediction of water quality. The RMSE is used to facilitate a comparison of the accuracy of the sampling data. In the training model, the training data have an RMSE of 20.89, and the testing data have an RMSE of 30.11. The predicted R-squared values in the RNN2 model are 0.993 (training data) and 0.941 (testing data)