Search

In order to achieve the Sustainable Development Goals (SDG), it is imperative to ensure the safety of drinking water. The characteristics of each drinkable water, encompassing taste, aroma, and appearance, are unique. Inadequate water infrastructure and treatment can affect these features and may also threaten public health. This study utilizes the Internet of Things (IoT) in developing a monitoring system, particularly for water quality, to reduce the risk of contracting diseases. Water quality components data, such as water temperature, alkalinity or acidity, and contaminants, were obtained through a series of linked sensors. An Arduino microcontroller board acquired all the data and the Narrow Band-IoT (NB-IoT) transmitted them to the web server. Due to limited human resources to observe the water quality physically, the monitoring was complemented by real-time notifications alerts via a telephone text messaging application. The water quality data were monitored using Grafana in web mode, and the binary classifiers of machine learning techniques were applied to predict whether the water was drinkable or not based on the data collected, which were stored in a database. The non-decision tree, as well as the decision tree, were evaluated based on the improvements of the artificial intelligence framework. With a ratio of 60% for data training: at 20% for data validation, and 10% for data testing, the performance of the decision tree (DT) model was more prominent in comparison with the Gradient Boosting (GB), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) modeling approaches. Through the monitoring and prediction of results, the authorities can sample the water sources every two weeks. [ABSTRACT FROM AUTHOR]

Summary: Purpose: The aim of this study was to evaluate the prevalence of prescription and use of antiepileptic drugs (AEDs) for the treatment of epilepsy and other indications in a nationwide population using a prescription database. Materials and methods: AED prescription data were collected from the National Health Insurance Research Database (NHIRD) in Taiwan for a 5-year period (2003–2007). Patients prescribed AEDs at least two times from 2003 to 2007 were selected for the study from a random sample that included approximately 600,000 people. Results: The prevalence of AED use (per 1000 inhabitants) increased from 12.6 in 2003 to 13.8 in 2007. The prevalence of newer AED use increased progressively from 1.0 in 2003 to 3.8 in 2007, but the prevalence of older AED use decreased during this time (11.6–10). Carbamazepine and valproic acid were the most common AEDs used. Among the newer generation of AEDs, gabapentin was the most frequently used. Newer AEDs were used primarily to treat pain disorders. The primary class of drugs used to treat epileptic disorders was older AEDs. Conclusion: An increase in the use of AEDs was observed over a 5-year period in data collected from NHIRD. This might implicate the use of newer compounds at clinical practice not only increased in the treatment of epilepsy, but also in the conditions other than epilepsy especially pain disorders. [Copyright &y& Elsevier]

Summary: Objective: The objective of this study was to analyze and evaluate antiepileptic drug (AED) utilization among adults in Taiwan. Methods: A random sample of 167,377 patients from the National Health Insurance (NHI) reference database was used. Prescription records were retrieved for all patients prescribed AEDs during 2004. The prescribed daily dose/defined daily dose (PDD/DDD) ratio was used to assess the adequacy of AED dosing. Results: Seventy-one percent (n =518) of patients used only one AED, while 29% (n =212) used more than two AEDs in 2004. For monotherapy, the most frequent regimens included carbamazepine (41.9%), followed by phenytoin (27.3%) and valproic acid (17.8%). For polytherapy, the most commonly used combination was valproic acid and carbamazepine. For adults, the mean PDD/DDD ratio for each AED used for either monotherapy or polytherapy was less than 1.00. Additionally, adult patients treated with more than one AED during 2004 in Taiwan took each drug in higher dose than patients using the same AED in monotherapy. Conclusion: In Taiwan antiepileptic drug therapies appear to be still dominated by the first generation drugs. The mean dosages of most antiepileptic drugs were lower than that of WHO suggested. [Copyright &y& Elsevier]

Summary: Objective: To determine the prevalence of treated epilepsy in the Taiwan in 2004 using prescription data from the National Health Insurance (NHI) research database and to compare this rate with estimates from other prevalence studies. Methods: A cohort was extracted from a group of antiepileptic drug (AED) users who met two inclusion criteria: (1) use of AED more than once and International Classification of Disease (ICD) codes indicating epilepsy or (2) use of AEDs except clonazepam for more than 90 days without ICD codes of epilepsy. Results: In total, 559 AED users were included. The estimated crude prevalence of AED use was 0.42% of the adult population (0.4% for women and 0.45% for men), and 72% of the cohort (n =400) used only one AED. Conclusion: Our estimate of prevalence of treated epilepsy through the NHI system has much larger coverage of population with more credible registration system, and appears to be closer to the truth than two previous small-scaled community-based studies in Taiwan. [Copyright &y& Elsevier]

The 2017 epilepsy and seizure diagnosis framework emphasizes epilepsy syndromes and the etiology-based approach. We developed a propositional artificial intelligence (AI) system based on the above concepts to support physicians in the diagnosis of epilepsy. We analyzed and built ontology knowledge for the classification of seizure patterns, epilepsy, epilepsy syndrome, and etiologies. Protégé ontology tool was applied in this study. In order to enable the system to be close to the inferential thinking of clinical experts, we classified and constructed knowledge of other epilepsy-related knowledge, including comorbidities, epilepsy imitators, epilepsy descriptors, characteristic electroencephalography (EEG) findings, treatments, etc. We used the Ontology Web Language with Description Logic (OWL-DL) and Semantic Web Rule Language (SWRL) to design rules for expressing the relationship between these ontologies. Dravet syndrome was taken as an illustration for epilepsy syndromes implementation. We designed an interface for the physician to enter the various characteristics of the patients. Clinical data of an 18-year-old boy with epilepsy was applied to the AI system. Through SWRL and reasoning engine Drool's execution, we successfully demonstrate the process of differential diagnosis. We developed a propositional AI system by using the OWL-DL/SWRL approach to deal with the complexity of current epilepsy diagnosis. The experience of this system, centered on the clinical epilepsy syndromes, paves a path to construct an AI system for further complicated epilepsy diagnosis. • We developed a propositional AI system by using the OWL-DL/SWRL approach. • The knowledge base contains concepts for the 2017 epilepsy diagnostic framework. • This system can infer the possible epilepsy syndromes and/or etiologies. • It will support physician to deal with the complexity of current epilepsy diagnosis. [ABSTRACT FROM AUTHOR]

The combination of edge and cloud computing is going to make the Internet of Things (IoT) rapid, light, and more reliable. IoT and cloud-edge computing are distinct disciples that have evolved separately over time. However, they are increasingly becoming interdependent, and are what the future holds. A crucial aspect is how to design a compound of cloud and edge computing architectures, and implement IoT effectively. In this paper, we proposed a combination of Cloud and Edge Computing architecture and built a set of an intelligent air-quality monitoring system in Tunghai University as a case study. In this case, we implemented container-based virtualization which constructs Kubernetes Minion (Nodes) in the Docker container service independently for each service on the Edge side. Finally, to monitor the high-performance computing systems, clusters, and networks, we used Ganglia Monitoring System. Ganglia collects relevant information such as Central Processing Unit (CPU), memory, network and usage of Protocol Data Unit (PDU) to monitor the power consumption and makes a measurement and evaluation for Kubernetes Pods. [ABSTRACT FROM AUTHOR]

Edge computing is a new paradigm for processing data at the edge of networks. There are a variety of edge computing scenarios, depending on the situation. In this paper, we investigate an architecture with heterogenous devices for intelligence aquaculture. The system will collect water sensor data and run real-time video-based fish detection with a Deep Learning algorithm and Deepstream. Each system was monitored to ensure all the architecture design was running properly. Kubernetes Lightweight Kubernetes (K3s) was used to manage all applications deployed in Docker containers. In addition, to synchronizing the heterogenous devices and visualizing the node’s resource system, the Rancher Kubernetes Engine is used to coordinate its resources. The container-based architecture with embedded GPU can work properly for fish detection and the POD schedule scheme that we propose show an improvement in container AI performance and increased around 20%. The architecture of this system can be referenced as a model for edge computing ecosystems. [ABSTRACT FROM AUTHOR]

The outreach of healthcare services is a challenge to remote areas with affected populations. Fortunately, remote health monitoring (RHM) has improved the hospital service quality and has proved its sustainable growth. However, the absence of security may breach the health insurance portability and accountability act (HIPAA), which has an exclusive set of rules for the privacy of medical data. Therefore, the goal of this work is to design and implement the adaptive Autonomous Protocol (AutoPro) on the patient's remote healthcare (RHC) monitoring data for the hospital using fully homomorphic encryption (FHE). The aim is to perform adaptive autonomous FHE computations on recent RHM data for providing health status reporting and maintaining the confidentiality of every patient. The autonomous protocol works independently within the group of prime hospital servers without the dependency on the third-party system. The adaptiveness of the protocol modes is based on the patient's affected level of slight, medium, and severe cases. Related applications are given as glucose monitoring for diabetes, digital blood pressure for stroke, pulse oximeter for COVID-19, electrocardiogram (ECG) for cardiac arrest, etc. The design for this work consists of an autonomous protocol, hospital servers combining multiple prime/local hospitals, and an algorithm based on fast fully homomorphic encryption over the torus (TFHE) library with a ring-variant by the Gentry, Sahai, and Waters (GSW) scheme. The concrete-ML model used within this work is trained using an open heart disease dataset from the UCI machine learning repository. Preprocessing is performed to recover the lost and incomplete data in the dataset. The concrete-ML model is evaluated both on the workstation and cloud server. Also, the FHE protocol is implemented on the AWS cloud network with performance details. The advantages entail providing confidentiality to the patient's data/report while saving the travel and waiting time for the hospital services. The patient's data will be completely confidential and can receive emergency services immediately. The FHE results show that the highest accuracy is achieved by support vector classification (SVC) of 88% and linear regression (LR) of 86% with the area under curve (AUC) of 91% and 90%, respectively. Ultimately, the FHE-based protocol presents a novel system that is successfully demonstrated on the cloud network. [ABSTRACT FROM AUTHOR]

To investigate the impact of an electronic medical record management system (EMRMS) on disease activity and the frequency of outpatient visits among patients with ankylosing spondylitis (AS). We identified 652 patients with AS who were followed up for at least 1 year before and after the first Ankylosing Spondylitis Disease Activity Score (ASDAS) assessment and compared the number of outpatient visits and average visit time within 1 year before and after the initial ASDAS assessment. Finally, we analyzed 201 patients with AS who had complete data and received ≥ 3 continuous ASDAS assessments at an interval of 3 months, and we compared the results of the second and third ASDAS assessments with those of the first. The number of annual outpatient visits increased after ASDAS assessment (4.0 (4.0, 7.0) vs. 4.0 (4.0, 8.0), p < 0.001), particularly among those with a high initial disease activity. The average visit time was reduced within 1 year after ASDAS assessment (6.4 (8.5, 11.2) vs. 6.3 (8.3, 10.8) min, p = 0.073), especially among patients whose with an inactive disease activity was < 1.3 (ASDAS C-reactive protein (CRP) 6.7 (8.8, 11.1) vs. 6.1 (8.0, 10.3) min, p = 0.033; ASDAS erythrocyte sedimentation rate (ESR) 6.4 (8.7, 11.1) vs. 6.1 (8.1, 10.0) min, p = 0.027). Among patients who received at least three ASDAS assessments, the third ASDAS-CRP tended to be lower than the first (1.5 (0.9, 2.1) vs. 1.4 (0.8, 1.9), p = 0.058). The use of an EMRMS increased the frequency of ambulatory visits among AS patients with high and very high disease activity and reduced the visit time among those with an inactive disease. Continual ASDAS assessments may help control the disease activity of patients with AS. [ABSTRACT FROM AUTHOR]

Time series prediction is a challenging predictive modeling case. It is essential to have a prediction model that can adapt to dynamic data. Air quality data show a significant changing degree of spatial and temporal data. Therefore, the updated deep learning model is suitable for this case. In this paper, monitoring and analysis of air quality with dynamic training using recurrent neural network (RNN) are proposed to provide the model remains up-to-date as new data comes. In the experiments, by adjusting the model, the accuracy is enhanced. The scheduling retrained model is provided based on the expected mean absolute percentage error (MAPE) value. First, the machine learning architecture environment is being prepared. Secondly, the RNN parameters were optimized for excellent level predictive precision. Third, set and test the scheduling and MAPE value based on the MAPE's expected value for the automatic retraining model. Finally, on the interactive map, the output is presented using R and Shiny to visualize the RNN training results. [ABSTRACT FROM AUTHOR]

Objective: To assess the associations of the Assessment of Spondyloarthritis International Society Health Index (ASAS HI) with gender and other factors in patients with ankylosing spondylitis (AS). Methods: From November 2017 to October 2018, we measured the Ankylosing Spondylitis Disease Activity Score (ASDAS), the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), the Bath Ankylosing Spondylitis Functional Index (BASFI), the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) and the ASAS HI score for AS patients at the Taichung Veterans General Hospital. After adjusting for disease activity (ASDAS-erythrocyte sedimentation rate [ESR], ASDAS- C-reactive protein [CRP], BASDAI+ESR or BASDAI+CRP), mSASSS and other potential confounders including medications, comorbidities, and laboratory data, any associations between gender and the sum score of ASDAS HI were assessed using multiple linear regression analysis, as well as any associations between gender and an ASAS HI score >5 using multivariable logistic regression analysis. Results: A total of 307 AS patients (62 [20.2%] females, mean age 46.4 years [S.D. 13.3], mean symptom duration 20.6 years [S.D. 12.1]) were included. Multiple linear regression analysis showed that the male gender was significantly associated with a lower ASAS HI (B = -1. 91, 95% confidence interval [CI], −2.82–−1.00, p <0.001). Multivariable logistic regression analysis revealed that males also had a lower risk of achieving scores of ASAS HI > 5 than females (odds ratio = 0.15, 95% CI, 0.07–0.36, p <0.001). Disease activity measures, including ASDAS-ESR, ASDAS-CRP and BASDAI, had positive correlations with ASAS HI. Conclusion: This single-center, cross-sectional study revealed that a higher ASAS HI score was significantly associated with female gender and higher disease activity measures. [ABSTRACT FROM AUTHOR]

The objective of the study was to explore the influences of seasonality, meteorological conditions, and air pollution exposure on the number of patients who visit the hospital due to seizures. Outpatient and inpatient data from the National Health Insurance Database of Taiwan from 2009 to 2013, meteorological data from the Meteorological Bureau, and air pollution exposure data from the Taiwan Air Quality Monitoring Stations were collected and integrated into daily time series data. The following data processing and analysis results are based on the mean of the 7 days' lag data of the 18 meteorological condition/air pollution exploratory factors to identify the critical meteorological conditions and air pollution exposure factors by executing univariate analysis. The average hospital visits for seizure per day by month were used as an index of observation. The effect of seasonality has also been examined. The average visits per day by month had a significant association with 10 variables. Overall, the number of visits due to these factors has been estimated to be 71.529 (13.7%). The most obvious factors affecting the estimated number of visits include ambient temperature, CH 4 , and NO. Six air pollutants, namely CH 4 , NO, CO, NO 2 , PM2.5, and NMHC had a significantly positive correlation with hospital visits due to seizures. Moreover, the average daily number of hospital visits was significantly high in January and February (winter season in Taiwan) than in other months (R2 = 0.422). The prediction model obtained in this study indicates the necessity of rigorous monitoring and early warning of these air pollutants and climate changes by governments. Additionally, the study provided a firm basis for establishing prediction models to be used by other countries or for other diseases. • AMB_TEMP and UBV had decrease risk on hospital-visits for seizures. • PS01, RAINFALL, CH 4 , NO, CO, NO 2 , PM2.5, and NMHC had increase risk. • The overall impact of these factors on the average visits per day by month was 13.7%. • The number of visits in January and February was significantly higher than in other months. [ABSTRACT FROM AUTHOR]