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

Androgen deprivation therapy (ADT) is frequently used to treat prostate cancer which is a widespread disease having a very low survival rate. A prolonged course of ADT can increase toxicity and drug resistance. This study proposes an adaptive therapy combining chemotherapy or immunotherapy with the discontinuation of hormone therapy to overcome these obstacles. The super-twisting sliding mode control (STSMC) algorithm is found to be one of the effective approach as an ADT model for obtaining suitable dosage adaptively. The primary objective is to rapidly reduce the number of cancer cells and the duration of drug exposure. The Takagi–Sugeno fuzzy controller-based active control algorithm is introduced, and it’s performance is compared with the STSMC algorithm. While maintaining global asymptotic stability, the Takagi–Sugeno fuzzy controller reduces the duration of therapy to six months. The controllers are implemented utilizing the linear matrix inequality (LMI) algorithm and the yet another LMI (YALMIP) toolset for MATLAB, and their efficacy is validated utilizing MATLAB and Simulink simulations. This study presents a novel approach to improve prostate cancer treatment outcomes by integrating nonlinear control algorithms and adaptive dosage strategies to reduce treatment duration and minimize drug exposure, thereby improving patient outcomes in prostate cancer management.

The recent outbreak of the novel coronavirus (COVID-19) pandemic has devastated many parts of the globe. Non-pharmaceutical interventions are the widely available measures to combat and control the COVID-19 pandemic. There is great concern over the rampant unaccounted cases of individuals skipping the border during this critical period in time. We develop a deterministic compartmental model to investigate the impact of escapees (individuals who evade mandatory quarantine) on the transmission dynamics of COVID-19. A suitable Lyapunov function has shown that the disease-free equilibrium is globally asymptotically stable, provided $R_0<1$. We performed a global sensitivity analysis using the Latin-hyper cube sampling method and partial rank correlation coefficients to determine the most influential model parameters on the short and long-term dynamics of the pandemic to minimize uncertainties associated with our variables and parameters. Results confirm a positive correlation between the number of escapees and the reported COVID-19 cases. It is shown that escapees are primarily responsible for the rapid increase in local transmissions. Also, the results from sensitivity analysis show that an increase in governmental role actions and a reduction in the illegal immigration rate will help to control and contain the disease spread.

A novel fractional derivative model with nine compartments is formulated to investigate the transmission dynamics of zika and dengue co-infection. The Atangana–Baleanu fractional derivative in the Caputo sense was employed. The conditions for a unique solution are identified, and the solutions’ positivity and boundedness are demonstrated. The disease-free equilibrium point (DFE) and basic reproduction number, R₀, were obtained. The DFE was shown to be locally asymptotically stable when the basic reproduction number is less than one. Zika-associated reproduction number, R_0z, and dengue-associated reproduction number, R_0d, were estimated to be 1.0144 and 1.1724, respectively. The system was shown to be generalized Ulam Hyers–Rassias stable, and the Adam–Bashforth method was used to provide its’ numerical solution. Sensitivity analysis using the Latin Hyper-cube Sampling (LHS) and Partial Rank Correlation Coefficient (PRCC) (|PRCC|> 0.45) with 200 runs was carried out using various variables as response functions per time. The most significant parameters were found to be zika human-to-human transmission rate, $\beta$ _hz1, vector death rate, $\mu$ _v, zika recovery rate, $\gamma$ _hz1 and dengue vector-to-human transmission rate, $\beta$ _hd. Real data from Espirito Santo in Brazil is used to validate the model and fit needed parameter values. Numerical simulations illustrated the impact of varying the fractional order derivative, recovery rates, transmission rates, and cross-enhancement parameters on the infected human compartments. The zika Human-to-human transmission rate, $\beta$ _hz1, was found to be a very significant parameter in the control of zika disease transmission. Increasing the vector death rate, $\mu$ _v, was more important in curbing dengue prevalence and incidence than the attainment of recovery from the dengue disease, and the absence of the zika Vector-to-human transmission rate, $\beta$ _hz3, was almost insignificant in the presence of the zika Human-to-human transmission rate, $\beta$ _hz1, for disease eradication. This study suggested control measures and strategies to decrease the dengue and zika human-to-human transmission rates.

The Centers for Medicare and Medicaid Services (CMS) provides annual reports of costs, charges, utilization, payment, penalty, payroll, and general institutional characteristics for thousands of Medicare-certified hospitals. However, beyond the small fraction of features offered in dated finalized public use files, the size and complexity of cost report data can make it difficult to use. To gain a greater breadth of up-to-date insights, hospitals and researchers must either pay for third party services or acquire the appropriate expertise. To democratize insights into cost report data, we first developed an open-source public repository of 6908 hospital-specific dataset, each containing 2843 labeled features and spanning years between 2010 and 2023. We then developed an open-source application for analyzing and downloading these data. Users can download and run the application locally or access it online (https://hcris-app.herokuapp.com/), and compare cost report features among hospitals and across time, explore relationships between features, and design new cost report variables. As examples of insights gained from our application, we present results from comparing Rush University Medical Center to 66 non-governmental acute care Illinois hospitals. We look forward to developing our open-source resources according to feedback from the healthcare community.

Diabetes disease seriously threatens people's health and is becoming more common nowadays. Diabetes Mellitus (DM) is a condition caused by high blood sugar levels, inactivity, unhealthy eating, being overweight, and other factors. This research article analyzed and examined various risk prediction models and algorithms for diabetes, including Type 1, Type 2, and Gestational Diabetes. This study develops several Machine Learning (ML) models for predicting diabetes using various datasets. The process involves producing highly informative features called Feature Engineering (FE). We used the Pima Indian Diabetes Dataset (PIDD) to experiment with and examine the effectiveness of ML models' ability to predict diabetes. Using Python programming, we used three classification algorithms, Logistic Regression, Gradient Boost, and Decision Tree, and combined feature selection techniques among the classification techniques, Decision Tree has the highest accuracy rate (91 %), precision (96 %), recall (92 %), and Fi score (94 %).

An infodemic is an information epidemic capable of compromising public health. This manuscript proposes an infoveillance method suitable for listening to web concerns on health to develop adequate infodemiological responses based on the World Health Organization indications. In particular, the case of COVID-19 vaccinations in Italy was investigated. Web interest and concern in COVID-19 vaccines over the past week (January 8–14, 2023) was investigated via the websites Google Trends and Talkwalker by searching for appropriate keywords. Thanks to the analysis of related queries and topics, it was possible to determine and examine the most debated topics relating to specific side effects. Emotional reactions regarding COVID-19 vaccines have been negative in varying percentages between 40 and 70 %, depending on the topic discussed. Feelings of alarm, derision, doubt, and anger were common (about 60 %). The concerns were mainly about the effectiveness against recent COVID-19 variants and alleged side effects such as sudden death, tumors, myocarditis, prion disease, and high ferritin. The most used media among those scrutinized was Twitter (over 90 % of interactions). The male audience participated more and showed more negativity than the female one. The age groups mainly involved were the under-45s. This research discussed the combined use of Google Trends and Talkwalker to conduct rapid infoveillance surveys. The results found showed that the web public has many doubts about COVID-19 vaccines, including the appearance of very rare or unproved side effects. Based on the WHO infodemic management strategy, it is essential that this or similar approaches are adopted by health and government authorities to listen to the community and calibrate appropriate infodemiological responses aimed at preserving public health.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) caused the start of the COVID-19 outbreak in the world, including Malaysia and Thailand. This study identifies the trend of the COVID-19 outbreak before and after the vaccination campaign by using the Susceptible-Exposed-Infectious-Recovered (SEIR) and Susceptible-Exposed-Infectious-Recovered-Vaccinated (SEIRV) models. Moreover, we predict the daily reported death and recovery cases using the SEIR model and Holt's linear trend method and then evaluate their performance. The data used in this study is real data from Malaysia and Thailand. The SEIRV model provides a comprehensive view of the efficacy of COVID-19 vaccinations in curbing the COVID-19 outbreak. This research reveals that the SEIR model outperforms Holt's linear trend method in predicting daily reported cases.

Deep learning methods in artificial intelligence are used for brain tumor diagnosis as they handle a huge amount of data. Compared to computerized tomography (CT), Ultrasound, and X-ray imaging, Magnetic Resonance Imaging (MRI) is effectively used for machine vision-based brain tumor diagnosis. However, due to the complex nature of the brain, brain tumor diagnosis is always challenging. This research aims to study the effectiveness of deep transfer learning architectures in brain tumor diagnosis. This paper applies four transfer learning architectures- InceptionV3, VGG19, DenseNet121, and MobileNet. We used a dataset with data from three benchmark databases of figshare, SARTAJ, and Br35H to validate the models. These databases have four classes: pituitary, no tumor, meningioma, and glioma. Image augmentation is applied to make the classes balanced. Experimental results demonstrate that the MobileNet outperforms competing methods by exhibiting an accuracy of 99.60%.

Diabetes is a chronic glucose metabolism disorder with severe clinical consequences. The prevalence of diabetes mellitus, in particular Type 2 Diabetes (T2D), is rising dramatically globally. Several clinical trials provide evidence that lifestyle interventions can prevent or delay the development of T2D, but the impact of lifestyle interventions is seldom investigated using a mathematical model. This study assesses the effects of lifestyle interventions on people by constructing an ordinary differential equation model. In this paper, a general model is developed based on the dynamics of T2D by incorporating a control variable termed as healthy lifestyle. The population is subdivided into five classes: susceptible, affected, treated, healthy lifestyle, and prevented. Sensitivity analysis has been performed to identify the most important parameters, and the stability of the equilibrium point is analyzed. Numerical simulations are conducted using a diabetes data set in Bangladesh to investigate the model's dynamic behavior. The results from this study reveal that maintaining a healthy lifestyle slows disease progression. The sensitivity analysis shows that the healthy lifestyle rate, treatment rate, and diabetes rate from susceptible and healthy lifestyle classes are the most sensitive parameters. Moreover, the study also concludes that diabetes cannot completely be eliminated, but with proper control measures, the burden can be reduced. The findings from the study provide strong reasons to continue implementing lifestyle interventions to prevent the global epidemic and its adverse effects.

The rise of drug resistance has become a major obstacle in treating tuberculosis (TB), significantly contributing to the increasing disease burden. Therefore, it is essential to study the transmission dynamics of the disease, considering the factors that contribute to the strain’s impact on the disease burden, using an epidemiological model. We present a deterministic mathematical model that explores the dynamics of TB with two strains: drug-susceptible and drug-resistant, taking into account exogenous re-infection. We thoroughly analyze to gain insights into the behavior of the model. The qualitative analysis of the model reveals an interesting phenomenon known as “backward bifurcation,” where both stable disease-free and stable endemic equilibria coexist when the associated reproduction number is less than one. In the absence of exogenous re-infection, the model shows the existence of unique positive endemic equilibria. Numerical simulations were conducted, yielding noteworthy results. Increasing the treatment rate for individuals infected with the drug-susceptible strain reduces the number of new cases of drug-susceptible TB while increasing the detection of drug-resistant TB cases. The simulations demonstrate that drug-susceptible and drug-resistant TB strains can coexist when their reproduction numbers exceed one without competitive exclusion occurring. In summary, this study sheds light on the challenges posed by drug resistance in TB treatment and highlights the importance of understanding the disease dynamics through mathematical modeling to develop effective strategies for its control.