Healthcare analytics (New York, N.Y.)最新文献

Income inequality is a prominent contributor to health disparities in the U.S. As a leading capitalist nation, the U.S. registers the highest healthcare expenditure among developed countries yet grapples with widening income disparities. The chasm between the rich and the underprivileged has expanded significantly in recent decades, profoundly impacting American society. This study explores the nuances of income inequality, its ramifications, and potential remedies, analyzed through the Gini Coefficient. Advanced forecasting models, including AutoRegressive Integrated Moving Average and Regression Analysis, are employed to anticipate future patterns. The research highlights the value of healthcare analytics in understanding the complexities of income inequality. The findings underscore the pressing need for effective policies to address this mounting challenge.

It is essential to monitor any health issues during pregnancy to ensure a safe delivery because pregnancy is crucial for both mother and child. However, developing countries have poor access to healthcare, making managing possible health risks during pregnancy challenging. An Internet of Things (IoT)-based Medical Cyber-Physical System (MCPS) can offer a valuable and affordable solution for anticipating and controlling health hazards during pregnancy to solve this issue. This paper presents the design and development of an MCPS for recognizing health risks in pregnant women in developing countries. The system collects key health metrics using temperature, blood pressure, glucose levels, and heart rate sensors. It automatically considers risk factors to predict health risks using Machine Learning (ML) and sends them to the nearest clinic or hospital. Patients can manually enter their risk factors into the program and talk with a doctor through it. The efficacy of the proposed MCPS is evaluated using a dataset of pregnant women, and the results demonstrate that the system can accurately detect health issues during pregnancy. Medical experts can.

enhance maternal and fetal health outcomes using the systems real-time data collecting and processing capabilities. Despite restricted access to healthcare in developing countries, the proposed MCPS provides a valuable and economical method of addressing pregnancy-related health risks. The MCPS can assist medical personnel in making quick and informed choices, enhancing the level of care provided to expectant mothers and their unborn children.

In recent years, age-related changes in body composition in the elderly are attracting attention. This is associated with a decline in physical functions and an increased risk of disease development. In general, age-related changes in body composition can be minimized with appropriate exercise. However, there are no studies that investigate body parts susceptibility to aging and changes in body composition of those parts. Therefore, devising exercise programs and advising daily life while taking these into account becomes difficult. This study aims to identify body parts that are more susceptible to aging and their body composition changes. The body composition was obtained with a Direct Segmental Multi-frequency Bioelectrical Impedance Analysis using InBody770 in 8 male elderly patients who had been shortly hospitalized. Statistical hypothesis testing was used to determine whether site-specific body composition changed significantly between hospital discharge and 1 year, 1 year and 2 years, and hospital discharge and 2 years. The results showed that Lean body mass, Total Body Water, Intracellular Water, Extracellular Water in the right arm; Lean body mass and Total Body Water in the left arm and trunk are more sensitive to aging.

This study explores the multispecies Lotka-Volterra population dynamics models, a captivating nonlinear mathematical framework with significant applications in natural sciences and environmental studies. The primary objective is to deliver precise solutions for these models using the New Iterative Method (NIM). Numerical simulations are conducted on three distinct types of nonlinear dynamic problems, comparing the accuracy of the NIM with that of the Perturbation Iteration Algorithm (PIA), existing exact solutions, and the traditional fourth-order Runge–Kutta method. A continuous step time of Δ = 0.001 was used for the Runge–Kutta method in all computations. Notably, the NIM's solutions for the nonlinear multispecies Lotka-Volterra models demonstrate very good accuracy, achieving convergence to the Runge–Kutta method's solutions within five iterations. The correctness of the NIM is found to be better than the other existing solutions. Its distinctive attribute lies in its computational efficiency, providing high accuracy without necessitating linearization, discretization, multipliers, or polynomials for nonlinear terms. This leads to simpler solution procedures while maintaining commendable accuracy. The findings underscore NIM's reliability and broad applicability in both linear and nonlinear models, highlighting its potential as an invaluable tool in numerical computation.

The Integration of machine learning and traditional image processing in dentistry has resulted in many applications like automatic teeth identification and numbering, caries, anomaly, disease detection, and dental treatment prediction. They have a broad scope in different applications observed in the dentistry literature review. This study reviews the literature on deep learning and dental radiograph analysis. We present an overview of machine learning algorithms in different areas of dentistry: tooth identification and numbering, Dental disease detection, and dental predictive treatment models. The methods under each area are briefly discussed. The dental radiograph data set required for performing experiments is summarized from the available literature. The study concludes by discussing new research opportunities and initiatives in this field. This paper offers a comprehensive overview of this innovative, challenging, and growing area in dentistry.

Blockchain technology and the Internet of Medical Things (IoMT) have garnered increased attention recently due to their growing application in effectively managing data security, storage, and transmission concerns within healthcare organizations. However, integrating various advancements, such as coordination, adaptivity, and automated responses, within the framework of blockchain-based IoMT has amplified its susceptibility to a range of attacks and vulnerabilities. Assessing and enhancing the resilience of blockchain-based IoMT is of utmost importance, particularly in anticipation of potential disruptions, to ensure its continuous and sustainable functionality. The stochastic nature of risks adds complexity to evaluating the resilience of blockchain-based IoMT, given that resilience in this domain may fluctuate over time. This study employs a dynamic Bayesian network (DBN) method to address the evolving characteristics of pertinent variables, capturing their temporal dependencies and demonstrating how the resilience capabilities of blockchain-based IoMT may evolve across different time intervals. Additionally, an information theory approach is adopted to mitigate uncertainty regarding the resilience performance of blockchain-based IoMT and its crucial subcomponents. This research showcases the effectiveness and adaptability of the DBN methodology in healthcare systems, offering insights for shaping appropriate and essential strategies for decision-makers to establish a highly resilient framework for blockchain-based IoMT.

Diphtheria is an infectious disease caused by a strain of Corynebacterium diphtheria and forms part of the childhood vaccine-preventable diseases. The Diphtheria vaccine is a component of one of the routine vaccines given to children thrice before their first birthday. The protection against diphtheria derived from the diphtheria vaccine in infancy wanes in later childhood, necessitating a booster dose to protect the child as they grow older. To determine the impact of a booster dose of the diphtheria vaccine amidst a contaminated environment, a diphtheria model that incorporates a vaccine booster and a contaminated environment is formulated. The reproduction number R0 is computed and used to prove the local and global stability of the disease-free equilibrium. Global sensitivity analysis is conducted via the application of Latin Hypercube Sampling (LHS) with a Partial Rank Correlation coefficient on the infected humans and the contaminated environment to deduce the most sensitive parameters of the dynamics of diphtheria disease. Then, the model is further extended based on the result of the global sensitivity analysis by introducing four time-dependent controls, disinfection, screening/treatment, booster vaccination, and hygiene practice, to form an optimal control model. The control model is analyzed using Pontryagin’s maximum principle. The numerical simulation shows that diphtheria disease will reduce drastically in the community if any control combination involves booster vaccination since the diphtheria vaccine in infancy wanes after ten years. In a situation where there are limited resources to implement all the controls simultaneously, it is recommended to implement any two of the combined controls: disinfection of the environment and administration of booster vaccination or screening/treatment of the asymptomatic infected and administration of booster vaccination. The study shows that the best combination is to disinfect the environment, screen/treat the asymptomatic infected humans, and administer booster vaccination to the community.

COVID-19 is a virus that can cause severe pneumonia, and the severity varies based on the patient's immune system. The rapid spread of the disease can be mitigated through automated detection, addressing the shortage of radiologists in medicine. This paper introduces the Modified-Inception V3 (MIn-V3) model, which utilizes feature fusion from the internal layers of Inception V3 to classify different diseases, including normal cases, COVID-19 positivity, viral pneumonia, and bacterial pneumonia. Additionally, transfer learning and fine-tuning techniques are applied to enhance accuracy. The performance of MIn-V3 is assessed by comparing it with pre-trained Deep Learning (DL) models, such as Inception-ResNet V2 (InRN-V2), Inception V3, and MobileNet V2. Experimental results demonstrate that the MIn-V3 model surpasses other pre-trained models with a classification accuracy of 96.33 %. Furthermore, integrating the MIn-V3 model into a mobile application enables rapid and accurate detection of COVID-19, thus playing a crucial role in advancing early diagnostics, which is essential for timely intervention and effective disease management.

Pneumonia is the leading infectious disease that threatens the lives of children under five and elders over 65. It is an infection that is commonly caused by Streptococcus pneumoniae. In this study, an age-structured (children and elders) model for pneumonia was formulated and analyzed to determine the impact of treatment and proper nutrition on the transmission dynamics of the disease in the two age groups. The effective reproduction number ($R_e$) was determined using the next-generation method. The disease-free equilibrium point was determined and found locally and globally asymptotically stable if $R_e<1$. Sensitivity analysis of the model parameters was performed using the normalized forward sensitivity index method, and the findings show that transmission rates are the most positive parameters to the effective reproduction number, while proper nutrition was the most negatively sensitive parameter. Additionally, numerical simulations were performed, and it was observed that the combination of proper nutrition and treatment was more effective in reducing the number of pneumonia-infected individuals. The study encourages the joint use of proper nutrition and treatment to control pneumonia transmission among children and elders, especially in the developing world, where economic constraints, infrastructure, and distribution challenges limit vaccine availability.