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

Home healthcare companies (HHC) have emerged as vital alternatives to traditional hospitals, particularly in meeting the healthcare needs of individuals within the comfort of their homes. The COVID-19 pandemic has amplified the significance of HHC services, offering a crucial alternative for patients and the elderly to follow quarantine protocols while receiving essential healthcare at home. Consequently, HHC companies must align their planning strategies with the World Health Organization (WHO) health guidelines. This research introduces a Mixed Integer Linear Programming (MILP) model tailored for home healthcare services during COVID-19, aiming to ensure strict adherence to quarantine protocols while enhancing service efficiency and quality. The proposed vehicle routing problem with pickup/delivery and time window formulation incorporates critical elements such as patient and caregiver classification, work and break regulations adherence, workload balancing, and multi-depot capabilities. The model addresses uncertain demand and service times through a stochastic programming approach to enhance practicality. K-means clustering is applied to streamline scenarios, with a sensitivity analysis determining the optimal number of clusters. Additionally, measures intrinsic to stochastic programming, such as the Expected Value of Perfect Information (EVPI) and Value of Stochastic Solution (VSS), are computed for comprehensive analysis.

Healthcare expenditure, a considerable proportion of national budgets, has risen rapidly. Consequently, considerable research is devoted to controlling healthcare costs. Many efforts are underway to improve medical price transparency. Price transparency will help patients become better informed, allowing them to shop for care they can afford, eventually leading to efficiency in healthcare markets. This first requires medical pricing data to be made available publicly. Since the raw pricing data can be large and cover multiple conditions, it is necessary to provide an engine to process the data to facilitate its usage and understanding. We recommend creating computational models that predict healthcare costs for various patient conditions and demographics. Patients and providers can interrogate the underlying data to understand the variation of healthcare costs concerning medical conditions and demographic variables of interest, including age. We demonstrate our approach by creating predictive models using recent machine learning techniques. We analyzed anonymous patient data from the New York State Statewide Planning and Research Cooperative System, consisting of 2.34 million records from 2019. We built models to predict costs from over two dozen patient variables, including diagnosis codes, severity of illness, age, and other demographic variables. We investigated three models: regression, decision trees, and random forests. These models are explainable. We analyzed features to determine those that were predictive of total costs. We determined that the diagnosis code, severity of illness, and length of stay were good predictors of total costs, whereas race and gender are not useful in predicting total costs. We obtained the best performance using a catboost regressor, which yielded an R2 score of 0.85, better than the values reported in the literature.

In recent decades, breast cancer has become the most prevalent type of cancer that impacts women in the world, which shows a significant risk to the death rates of women. Early identification of breast cancer might drastically decrease patient mortality and greatly improve the chance of an effective treatment. In modern times, machine learning models have become crucial for classifying cancer and strengthening both the accuracy and efficiency of diagnostic and medical treatment strategies. Therefore, this study is focused on early detection of breast cancer using a variety of machine learning algorithms and desires to identify the most effective feature selection process with an amalgamated dataset. Initially, we evaluated five traditional models and two meta-models on separate datasets. To find the most valuable features, the study used the Least Absolute Shrinkage and Selection Operator (LASSO) as well as SHapley Additive exPlanations (SHAP) selection methods and analyzed them through a wide range of performance regulations. Additionally, we applied these models to the combined dataset and observed that the mergeddataset was significantly beneficial for breast cancer diagnosis. After analyzing the feature selection strategies, it was demonstrated that the majority of models performed more accurately when utilizing SHAP methodologies. Notably, three traditional models and two meta-classifiers obtained an accuracy of 99.82%, demonstrating superior performance compared to state-of-the-art methods. This advancement holds a crucial role as it lays the foundation for refining diagnostic tools and enhancing the progression of medical science in this field.

The tests for tracking diseases in newborns available through the National Neonatal Screening Program of the Brazilian Unified Health Care System cover six diseases. Mass spectrometer equipment is needed to expand and more efficiently and effectively detect new diseases. However, only four neonatal screening centers have the equipment capable of carrying out the extended test, and the expansion of health service capacity should consider both the rationalization of costs and the comprehensiveness and accessibility of care to the population. This study uses analytics to analyze and estimate the cost of centralized or distributed logistics networks and the level of service to perform the expanded test for newborns throughout Brazil. We evaluate the accessibility of the current infrastructure for the neonatal screening program and propose a novel location–allocation model to create a more integrated infrastructure for reducing disparities and increase the accessibility to neonatal screening services.

Our emotional, psychological, and social well-being are all parts of our mental health, influencing our thoughts, emotions, and behaviors. Mental health also influences how we respond to stress, interact with others, and make good or bad decisions. There has been growing interest in the use of machine learning for the early detection of mental illness. This study reviews the machine learning models, algorithms, and applications for the early detection of mental disease, particularly emphasizing the data modalities. We further propose a comprehensive methodology for assessing mental health that synergistically combines social media monitoring, data analytics from wearable devices, verbal polls, and individualized support. We provide an overview of the field’s current state, highlight the potential benefits and challenges of using machine learning in mental health care, and a new taxonomy of mental disorders issues based on five domains of data types. We review existing research on using machine learning to detect and treat mental illness and discuss the implications for future research. Finally, the value of this work lies in its potential to provide a fast and accurate method for predicting the mental health status of a person, which may assist in the diagnosis and treatment of mental illness.

The rapid spread of coronavirus disease 2019 (COVID-19) initially presented unprecedented challenges for clinicians, policymakers, and healthcare systems, as there was limited evidence on the efficacy of various control measures. This study endeavors to provide a detailed and comprehensive overview of the global progression of the COVID-19 mortality in the context of vaccine rollout, utilizing public surveillance data from 145 countries sourced from the World Health Organization and the World Bank. The primary focus is to analyze shifts in the trend of new COVID-19 mortality worldwide before and after the introduction of COVID-19 vaccines. To achieve this, we propose a longitudinal mixed effects model aimed at elucidating the relationship between mortality trend and vaccination rollout, alongside other pertinent covariates. Our modeling approach seeks to accommodate variations in the timing of COVID-19 vaccine rollout among countries, as well as the correlation of observations from within the same country. Our findings highlight the significant impact of new cases, cardiovascular death rate, senior population, stringency index, and reproduction rate on mortality. However, we find that the impact of vaccination is not statistically significant, as evidenced by a relatively large $p$-value. Furthermore, the study reveals substantial disparities in mortality rates among countries across four income groups.

Diabetic retinopathy (DR) is a severe global issue causing blindness if untreated, affecting millions worldwide and worsening over time. Addressing this growing concern necessitates early and accurate DR identification. This study introduces a novel approach to DR detection, combining machine learning algorithms with deep feature extraction techniques. A hybrid model is proposed by stacking predictions from diverse classifiers, such as Decision Trees, Random Forests, Support Vector Machines (SVMs), and more. Three deep learning models – MobileNetV2, DenseNet121, and InceptionResNetV2 – are employed as feature extractors from retinal images. Each classifier undergoes hyperparameter tuning for optimal performance. Using the APTOS 2019 Blindness Detection dataset, including preprocessing techniques like data augmentation and standardization, this hybrid model demonstrates promising accuracy in multi-class (95.50%) and binary classification (98.36%). Notably, DenseNet121 outperforms others. The results suggest the effectiveness of this hybrid technique in early diabetic retinopathy detection, holding significant promise for improved medical intervention.

Listeriosis is a food-borne disease that mainly affects pregnant women and newborns. We propose and analyze a deterministic model of Listeriosis by considering three groups of individuals: newborns, pregnant women, and others. Mathematical analysis of the model is performed, and equilibrium points are determined. The model has three equilibria, namely, the disease-free equilibrium, the bacteria-free equilibrium, and the endemic equilibrium. We use Castillo-Chavez theorem to establish the global stability of the disease-free equilibrium when the basic reproduction number is less than 1. The local asymptotic stability of the bacteria-free, and endemic equilibria are also established using the sign of the eigenvalues of the Jacobian matrix. We use the non-standard finite difference scheme and carried numerical simulations to confirm the theoretical results. We further show the impact of specific parameters on the dynamics of infectious individuals and observe that intervention is required in all the sub-populations by reducing the contact rate and vertical transmission to mininmize the number of infectious.