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

The outbreak of COVID-19 unleashed an unprecedented global pandemic, profoundly impacting lives and economies worldwide. Recognizing its severity, the World Health Organization (WHO) swiftly declared it a public health emergency of international concern. In response to this crisis, collaborative efforts have been underway to control the disease and minimize its health and socio-economic impacts worldwide. The COVID-19 epidemic curve holds vital insights into the history of exposure, transmission, testing, tracing, social distancing measures, community lockdowns, quarantine, isolation, and treatment, offering a comprehensive perspective on the nation’s response. One approach to gaining crucial insights is through meticulous analysis of available datasets, empowering us to effectively inform future strategies and responses. This study aims to provide descriptive data analytics of the COVID-19 pandemic in the Philippines, summarizing the country’s fight by visualizing epidemiological and mobility datasets, revisiting scientific papers and news articles, and creating a timeline of the critical issues faced during the pandemic. By leveraging these multifaceted analyses, policymakers and health authorities can make informed decisions to enhance preparedness, expand inter-agency cooperation, and effectively combat future public health crises. This study seeks to serve as a valuable resource, guiding nations worldwide in comprehending and responding to the challenges posed by COVID-19 and beyond.

This study presents a nonlinear mathematical model to capture the constant rates of three different target cells class-specific drug therapeutic measures (namely, drug therapy for blocking new infections, drug therapy for actively infected cells, and drug therapy inhibiting viral production) for the dynamics of within-host viral infections with multiple classes of target cells. The threshold quantity of the control reproduction number of the model is calculated. The global asymptotic behaviours of the model around the steady states are investigated in terms of the control reproduction number. Moreover, the model is extended to an optimal control problem by considering the three constant parameters for drug therapeutic measures as time-dependent control variables. Qualitative analysis of the proposed model is conducted using optimal control theory. Numerical solutions of the derived optimality system are sought to illustrate the efficacies of different combination strategies consisting of using at least any of the three target cells’ class-specific optimal controls in reducing the burden of within-host virus transmission and spread at a minimum cost. Cost-effectiveness analysis is further carried out to determine the least costly and most effective intervention strategy. The cost analysis reveals that the use of only target cells class-specific drug therapy control for blocking new infections is the most cost-effective control strategy.

This study aims to identify challenges in implementing a quality healthcare referral system in developing countries and explore the strategies to overcome these challenges. Data for this study were collected through consultations with experts in the field. We introduce a novel hybrid method called Criteria Importance Assessment (CIMAS) and Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN). CIMAS determines the relative importance of criteria, and AROMAN is employed to rank the strategies. The primary challenges identified include inadequate infrastructure facilities and deficient health information systems. The most appropriate strategy involves focusing on improving infrastructure facilities. We also carry out comprehensive sensitivity and comparative analyses to validate the applicability of the proposed model. This study identifies and elucidates the challenges of establishing a high-quality healthcare referral system in developing countries and substantially contributes to the existing body of knowledge by effectively delineating and prioritizing the strategies to tackle these challenges.

Regression is widely used in healthcare analytics, whether for examining hospital quality and safety, characterizing patterns of patient volume and healthcare costs, or predicting patient outcomes. Simple linear regression and other basic forms can be conducted with spreadsheet programs and are useful for examining simple linear relationships. However, expert statistical knowledge, computational skills, and specialized tools may be needed to characterize nonlinear relationships and complex interactions, to examine data that fail the assumptions of linear regression, to identify confounding variables and lessen the influence of outliers, and to build and evaluate predictive models. We constructed the Rush Regression Workbench to accomplish these tasks and to automate cautious and sophisticated analyses, provide interpretive outputs, enable reproducible results, and to provide the community with an evolving open-source good containing a diverse set of analyses and a growing library of over 170 preprocessed public healthcare datasets. The Rush Regression Workbench can be accessed via the web or downloaded and used locally.

The American healthcare system allocates considerable resources compared to peer-developed nations. However, outcomes significantly trail behind, particularly in life expectancy. This study addresses questions about the enduring trends in healthcare spending as a percentage of Gross Domestic Product (GDP), notable factors contributing to this concerning trend, and the timing to apply an emergency brake to curb this accelerating trajectory. Advanced machine learning algorithms, such as Random Forest and Support Vector Regression (SVR), in conjunction with traditional statistical forecasting methods, are used to forecast future patterns. The research underscores the importance of healthcare analytics in unraveling the intricacies of the healthcare system. The findings highlight the pressing need for effective policies to confront this mounting challenge.

Chronic liver disease (CLD) is a major health concern for millions of people all over the globe. Early prediction and identification are critical for taking appropriate action at the earliest stages of the disease. Implementing machine learning methods in predicting CLD can greatly improve medical outcomes, reduce the burden of the condition, and promote proactive and preventive healthcare practices for those at risk. However, traditional machine learning has some limitations which can be mitigated through ensemble learning. Boosting is the most advantageous ensemble learning approach. This study aims to improve the performance of the available boosting techniques for CLD prediction. Seven popular boosting algorithms of Gradient Boosting (GB), AdaBoost, LogitBoost, SGBoost, XGBoost, LightGBM, and CatBoost, and two publicly available popular CLD datasets (Liver disease patient dataset (LDPD) and Indian liver disease patient dataset (ILPD)) of dissimilar size and demography are considered in this study. The features of the datasets are ascertained by exploratory data analysis. Additionally, hyperparameter tuning, normalisation, and upsampling are used for predictive analytics. The proportional importance of every feature contributing to CLD for every algorithm is assessed. Each algorithm's performance on both datasets is assessed using k-fold cross-validation, twelve metrics, and runtime. Among the five boosting algorithms, GB emerged as the best overall performer for both datasets. It attained 98.80% and 98.29% accuracy rates for LDPD and ILPD, respectively. GB also outperformed other boosting algorithms regarding other performance metrics except runtime.

One of the realities of the COVID-19 worldwide pandemic is the occurrence of infected individuals with COVID-19 and two other diseases, Monkeypox and HIV. This study presents a compartmental deterministic epidemiological model with non-linear differential equations to study the transmission dynamics of the co-infection of the three diseases. Rigorous analysis of the model shows that the disease-free equilibrium was locally and globally asymptotically stable when the associated reproduction number of the diseases was not up to unity, showing that the spread of the diseases and their co-circulation can be effectively controlled in this circumstance. Real-life data about the diseases are collated and fitted to the model through which values of key parameters of the model were estimated. These parameters’ values were used to carry out numerical simulations of the model using MATLAB and validate the qualitative results obtained earlier from the model. The numerical simulation of the model was used to explore the interactions and dynamics resulting from the co-infection of COVID-19, HIV, and Monkeypox in humans, including the reciprocal impacts of each of the diseases on the other two, their patterns of coexistence and their effects on the host. We developed a tool to help predict the co-infection of the three diseases. Through the insights gained in this study, recommendations were made to policymakers in the healthcare sector on how to combat effectively and adequately the co-infection of the three diseases in the human population and mitigate their disease burden.

Neuronal cell segmentation identifies and separates individual neurons in an image, typically to study their properties or analyze their organization in the nervous system. This is significant because neurological problems and diseases can only be treated effectively when the structure and function of neurons are understood. The proposed method is based on convolutional neural networks (CNNs) and graph attention networks (GATs) for segmenting biomedical images. A contracting path built upon a couple of convolution layers and max pooling is included in the architecture to capture context. After that, the GATs are applied to the captured context. In GATs, each node in the graph is associated with a vector of hidden features, and the model calculates attention coefficients between pairs of nodes. These attention coefficients are learned during training and can be used to weigh the contribution of each node’s features to the representation of the graph. An expanding path that utilizes the outputs generated by GATs paves the way for exact segmentation. The dataset comprises 606 microscopic images, mainly categorized into different cell types (astrocytes, cortex, and SHSY5Y). By implementing our proposed U-GAT algorithm, we obtained the highest accuracy of 86.5% and an F1 score of 0.719 compared to the CNN, U-Net, SegResNet, SegNet VGG16, and GAT benchmarking algorithms. This proposed method could help researchers in the biotech industry develop novel drugs since a more accurate deep-learning method is essential for segmenting complex images like neuronal images.

This study presents the influence of COVID-19 and the pandemic on individuals diagnosed with hepatocellular carcinoma and intrahepatic cholangiocarcinoma, the two most common types of primary liver cancer. The study compares the effects before and after the pandemic on these patients. Additionally, it endeavors to predict the likelihood of survival for liver cancer patients. Our research will employ various methodologies to investigate this. Exploratory data analysis techniques are utilized, including univariate analysis, correlation analysis, bivariate analysis, chi-square testing, and T-sample testing. For predictive analytics, machine learning algorithms such as Logistic Regression, Decision Trees, Classification And Regression Tree (CART), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Support Vector Machines (SVMs) will be applied. For our outputs, Logistic Regression and SVMs emerged as top-performing algorithms, boasting a remarkable accuracy rate of 93%. The study reveals that COVID-19 affected all age groups similarly. However, a gender-based difference was observed, indicating that males faced a higher risk of both cancer and mortality. Furthermore, the study found that variables such as year, month, bleeding, cirrhosis, and previously known cirrhosis did not significantly influence patient survival.

The post-COVID-19 landscape has propelled the global telemedicine sector to a projected valuation of USD 91.2 billion by 2022, with a remarkable compounded annual growth rate (CAGR) of 18.6% from 2023 to 2030. This paper introduces an analytical wearable healthcare monitoring device (WHMD) designed for the timely detection and seamless transmission of crucial health vitals to telemedical cloud agents. The fractional order modeling approach is employed to delineate the efficacy of the WHMD in pregnancy-related contexts. The Caputo fractional calculus framework is harnessed to show the device potential in capturing and communicating vital health data to medical experts precisely at the cloud layer. Our formulation establishes the fractional order model's positivity, existence, and uniqueness, substantiating its mathematical validity. The investigation comprises two major equilibrium points: the disease-free equilibrium and the equilibrium accounting for disease presence, both interconnected with the WHMD. The paper explores the impact of integrating the WHMD during pregnancy cycles. Analytical findings show that the basic reproduction number remains below unity, showing the WHMD efficacy in mitigating health complications. Furthermore, the fractional multi-stage differential transform method (FMSDTM) facilitates optimal control scenarios involving WHMD utilisation among pregnant patients. The proposed approach exhibits robustness and conclusively elucidates the dynamic potential of WHMD in supporting maternal health and disease control throughout pregnancy. This paper significantly contributes to the evolving landscape of analytical wearable healthcare research, highlighting the critical role of WHMDs in safeguarding maternal well-being and mitigating disease risks in edge reconfigurable health architectures.