Analyzing Monthly Blood Test Data to Forecast 30-Day Hospital Readmissions among Maintenance Hemodialysis Patients

Background: The increase in the global population of hemodialysis patients is linked to aging demographics and the prevalence of conditions such as arterial hypertension and diabetes mellitus. While previous research in hemodialysis has mainly focused on mortality predictions, there is a gap in studies targeting short-term hospitalization predictions using detailed, monthly blood test data. Methods: This study employs advanced data preprocessing and machine learning techniques to predict hospitalizations within a 30-day period among hemodialysis patients. Initial steps include employing K-Nearest Neighbor (KNN) imputation to address missing data and using the Synthesized Minority Oversampling Technique (SMOTE) to ensure data balance. The study then applies a Support Vector Machine (SVM) algorithm for the predictive analysis, with an additional enhancement through ensemble learning techniques, in order to improve prediction accuracy. Results: The application of SVM in predicting hospitalizations within a 30-day period among hemodialysis patients resulted in an impressive accuracy rate of 93%. This accuracy rate further improved to 96% upon incorporating ensemble learning methods, demonstrating the efficacy of the chosen machine learning approach in this context. Conclusions: This study highlights the potential of utilizing machine learning to predict hospital readmissions within a 30-day period among hemodialysis patients based on monthly blood test data. It represents a significant leap towards precision medicine and personalized healthcare for this patient group, suggesting a paradigm shift in patient care through the proactive identification of hospitalization risks.

Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance

Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during influenza outbreaks. It compared the predictive capabilities of time-series and deep learning models against ILI emergency incidents. Results: The GT searches for "fever" and "cough" were significantly associated with ILI cases (p < 0.05). Temperature had a more substantial impact on ILI incidence than humidity. Among the tested models, ARIMA provided the best predictive power. Conclusions: GT and climate data can forecast ILI trends, aiding governmental decision making. Temperature is a crucial predictor, and ARIMA models excel in forecasting ILI incidences.

Dysarthria Speech Detection Using Convolutional Neural Networks with Gated Recurrent Unit

In recent years, due to the rise in the population and aging, the prevalence of neurological diseases is also increasing year by year. Among these patients with Parkinson’s disease, stroke, cerebral palsy, and other neurological symptoms, dysarthria often appears. If these dysarthria patients are not quickly detected and treated, it is easy to cause difficulties in disease course management. When the symptoms worsen, they can also affect the patient’s psychology and physiology. Most of the past studies on dysarthria detection used machine learning or deep learning models as classification models. This study proposes an integrated CNN-GRU model with convolutional neural networks and gated recurrent units to detect dysarthria. The experimental results show that the CNN-GRU model proposed in this study has the highest accuracy of 98.38%, which is superior to other research models.

An International Federal Hyperledger Fabric Verification Framework for Digital COVID-19 Vaccine Passport

The COVID-19 virus has been spreading worldwide on a large scale since 2019, and the most effective way to prevent COVID-19 is to vaccinate. In order to prove that vaccination has been administered to allow access to different areas, paper vaccine passports are produced. However, paper vaccine passport records are vulnerable to counterfeiting or abuse. Previous research has suggested that issuing certificates digitally is an easier way to verify them. This study used the consortium blockchain based on Hyperledger Fabric to upload the digital vaccine passport (DVP) to the blockchain network. In order to enable collaboration across multiple systems, networks, and organizations in different trust realms. Federated Identity Management is considered a promising approach to facilitate secure resource sharing between collaborating partners. Therefore, the international federal identity management architecture proposed in this study enables inspectors in any country to verify the authenticity of the DVP of incoming passengers using the consortium blockchain. Through practical construction, the international federal Hyperledger verification framework for the DVP proposed in this study has shown the feasibility of issuing a global DVP in safety analysis and efficacy testing.

An Azure ACES Early Warning System for Air Quality Index Deteriorating

With the development of industrialization and urbanization, air pollution in many countries has become more serious and has affected people's health. The air quality has been continuously concerned by environmental managers and the public. Therefore, accurate air quality deterioration warning system can avoid health hazards. In this study, an air quality index (AQI) warning system based on Azure cloud computing platform is proposed. The prediction model is based on DFR (Decision Forest Regression), NNR (Neural Network Regression), and LR (Linear Regression) machine learning algorithms. The best algorithm was selected to calculate the 6 pollutants required for the AQI calculation of the air quality monitoring in real time. The experimental results show that the LR algorithm has the best performance, and the method of this study has a good prediction on the AQI index warning for the next one to three hours. Based on the ACES system proposed, it is hoped that it can prevent personal health hazards and help to reduce medical costs in public.

Cloud-assisted mutual authentication and privacy preservation protocol for telecare medical information systems

BACKGROUND AND OBJECTIVE

 With the rapid development of wireless communication technologies and the growing prevalence of smart devices, telecare medical information system (TMIS) allows patients to receive medical treatments from the doctors via Internet technology without visiting hospitals in person. By adopting mobile device, cloud-assisted platform and wireless body area network, the patients can collect their physiological conditions and upload them to medical cloud via their mobile devices, enabling caregivers or doctors to provide patients with appropriate treatments at anytime and anywhere. In order to protect the medical privacy of the patient and guarantee reliability of the system, before accessing the TMIS, all system participants must be authenticated.

METHODS

 Mohit et al. recently suggested a lightweight authentication protocol for cloud-based health care system. They claimed their protocol ensures resilience of all well-known security attacks and has several important features such as mutual authentication and patient anonymity. In this paper, we demonstrate that Mohit et al.'s authentication protocol has various security flaws and we further introduce an enhanced version of their protocol for cloud-assisted TMIS, which can ensure patient anonymity and patient unlinkability and prevent the security threats of report revelation and report forgery attacks.

RESULTS

 The security analysis proves that our enhanced protocol is secure against various known attacks as well as found in Mohit et al.'s protocol. Compared with existing related protocols, our enhanced protocol keeps the merits of all desirable security requirements and also maintains the efficiency in terms of computation costs for cloud-assisted TMIS.

CONCLUSIONS

 We propose a more secure mutual authentication and privacy preservation protocol for cloud-assisted TMIS, which fixes the mentioned security weaknesses found in Mohit et al.'s protocol. According to our analysis, our authentication protocol satisfies most functionality features for privacy preservation and effectively cope with cloud-assisted TMIS with better efficiency.

An APN model for Arrhythmic beat classification

MOTIVATION

Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. Therefore, the ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this article, classification by using associative Petri net (APN) for personalized ECG-arrhythmia-pattern identification is proposed for the first time in literature.

RESULTS

A rule-based classification model and reasoning algorithm of APN are created for ECG arrhythmias classification. The performance evaluation using MIT-BIH arrhythmia database shows that our approach compares well with other reported studies.

DIFFERENTIATING PHYSIOLOGICAL EFFECTS OF MIDTERM BREAK IN A PROLONGED ONLINE GAME PLAYING

With the development of widespread Internet access, online game playing has become a popular event. As a result, more attention is being paid to the potentially negative physical and psychological effects on prolonged computer worker or online game player. To date, related physical problems that have been identified include fatigue, physical pain, insomnia, epileptic seizures, and even sudden death. Therefore, midterm break may be necessary for an online game player. This study tries to differentiate the physiological effects of two groups, with and without a midterm break, during a prolonged online game playing. Our experimental results showed that sympathetic system is overwhelming the parasympathetic nervous system significantly in group B without break, while it remains unchanged in group A. Due to the fight or flight response of the sympathetic system, participants with midterm break wanted to play more after event. Participants without break did not feel stressed after a continuous two hours' online game playing. These interesting findings may need further investigation in the future.

An embedded mobile ECG reasoning system for elderly patients

With the increase in the number senior citizens and chronic diseases, the number of elderly patients who need constant assistance has increased. One key point of all critical care for elderly patient is the continuous monitoring of their vital signs. Among these, the ECG signal is used for noninvasive diagnosis of cardiovascular diseases. Also, there is a pressing need to have a proper system in place for patient identification. Errors in patient identification, and hence improper administration of medication can lead to disastrous results. This paper proposes a novel embedded mobile ECG reasoning system that integrates ECG signal reasoning and RF identification together to monitor an elderly patient. As a result, our proposed method has a good accuracy in heart beat recognition, and enables continuous monitoring and identification of the elderly patient when alone. Moreover, in order to examine and validate our proposed system, we propose a managerial research model to test whether it can be implemented in a medical organization. The results prove that the mobility, usability, and performance of our proposed system have impacts on the user's attitude, and there is a significant positive relation between the user's attitude and the intent to use our proposed system.

The synthesis of 2-(functionalized methyl)-1 beta-methylcarbapenems

The synthesis of 1 beta-methylcarbapenems having a ROCH2 substituent at the 2-position is described. Their in vitro antibacterial activity and DHP-I susceptibilities are presented.

The structure of efrotomycin

The antibiotic efrotomycin (I), C59H88N2O20, was isolated from cultures of Nocardia lactamdurans as an amorphous yellow powder. Mass spectral and NMR analyses show that the compound is a glycoside of the known antibiotic aurodox (II), C44H62N2O12. Ozonolysis and hydrolysis of I produced the disaccharide V, 6-deoxy-4-O-(6-deoxy-2,4-di-O-methyl-alpha -L-mannopyranosyl)-3-O-methyl-beta-D-allopyranose. This disaccharide is attached to the 4-hydroxyl group of the hexahydropyran substructure of aurodox via a beta-linkage to C-1 of the allose.

Preparation and antibacterial activity of delta 1-thienamycin

delta 1-Thienamycin (2), a double-bond isomer of thienamycin, was prepared by isomerizing N-[[(p-nitrobenzyl)oxy]-carbonyl]thienamycin p-nitrobenzyl ester (5b) with DBU in Me2SO followed by hydrogenolysis of the protecting groups. When evaluated in a disc-diffusion antibacterial assay, delta 1-thienamycin was found to be essentially devoid of activity. The lack of antibacterial activity was ascribed to a chemically less reactive beta-lactam amide bond than that found in thienamycin.