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

Diagnosing brain tumors is difficult, especially at an early stage of the disease. Conventional approaches often cause delays in providing required treatment to the patients and shorten their lifespan. This paper presents a novel integrated approach with advanced subsets of artificial intelligence, including deep learning and supervised learning algorithms. These new technologies have demonstrated outstanding potential due to their ability to capture the appropriate features based on the input data. They can assist medical experts in identifying abnormal growth of cells inside the brain. We use publicly available brain magnetic resonance imaging (MRI) datasets to diagnose brain tumors and develop an automated system. The proposed approach uses data augmentation to enhance the image sizes and maintain standardization. We then deploy a visual geometry group with 16 layers following transfer learning to help minimize the medical experts’ workload in making accurate decisions. We extract the most significant features and improve the diagnostic speed and accuracy using a supervised learning algorithm and linear support vector machines (SVM). The proposed model outperforms the existing approaches with an accuracy of 98.87%, precision of 99.09%, recall of 98.73%, specificity of 99.02%, and F1-score of 98.91%.

Understanding population migration is essential for controlling highly infectious diseases. This paper studies the global dynamics of an infectious disease epidemic model incorporating population migration and a stochastic transmission rate. Our findings demonstrate that in deterministic and stochastic environments, the models exhibit global Lyapunov stability near the disease-free equilibrium point, determined by a threshold parameter. Furthermore, we analyze the effect of migration on infectious diseases. We discover that the number of infections and the peak value of the infection curve increase with a higher level of population migration. These results are supported by numerical illustrations that hold epidemiological relevance. Additionally, the disease-free equilibrium of the associated time delay model is linearly asymptotically stable, and the endemic equilibrium exhibits more bifurcation for larger time delay values.

Efficiency is critical in allocating sustainable healthcare resources to ensure that hospitals can effectively care for patients while maintaining high-quality care delivery. Hence, it is necessary to monitor efficiency carefully. This study aims to assess hospital unit effectiveness through a novel comprehensive approach integrating Multiple-Criteria Decision Making (MCDM) with Data Envelopment Analysis (DEA). The proposed MCDM-DEA framework involves allocating varying weights to distinct data categories. It harnesses the capabilities of the q-rung orthopair fuzzy (q-ROF) methodology to address the inherent uncertainties in healthcare performance assessment. The experimental results provide a comprehensively structured ranking system for specific hospital departments. This ranking system allows decision-makers to identify the strengths and weaknesses of each department, enabling them to make informed decisions regarding resource allocation and improvement strategies. Furthermore, the integration of MCDM-DEA provides a robust and objective assessment tool for monitoring and evaluating the performance of hospital departments over time. These rankings offer invaluable insights to decision-makers, equipping them with the strategic information needed to enhance the overall performance of hospital units.

Pharmaceutical industries in most developing countries with limited resources are expected to encounter several barriers while incorporating Industry 4.0 to transform into Pharma 4.0. With limited resources, a developing country must prioritize the barriers consider their impacts, and make a resource utilization plan accordingly. In this study, We employed a hierarchical multiple criteria decision analysis (MCDM) technique to identify potential barriers to Pharma 4.0 in developing countries and examine their effects to generate a prioritization inventory. Firstly, we extracted the likely barriers using a systematic literature study and used an expert opinion-based Delphi Method to choose the most pertinent barriers. Subsequently, we analyzed the correlation and influence of the selected barriers on each other by formulating a hierarchical multi-criteria model integrating Interpretive Structural Modelling (ISM) and the Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). As an outcome, we found three distinct categories of the selected 12 barriers: Prominent (4 of 12), Influencing (5 of 12), and Resulting (3 of 12). The results of this study are intended to assist the government in developing a solid adoption strategy for Pharma 4.0 and supply chain strategists in ensuring optimum resource utilization by resolving the examined barriers during the deployment of Pharma 4.0. The study is the first of its kind to discover barriers to Pharma 4.0 adoption and create hierarchical correlations within the context of the pharmaceutical sector from the perspective of a developing country.

This study leverages machine learning to enhance the diagnostic accuracy of COVID-19 using chest X-rays. The study evaluates various architectures, including efficient neural networks (EfficientNet), multiscale vision transformers (MViT), efficient vision transformers (EfficientViT), and vision transformers (ViT), against a comprehensive open-source dataset comprising 3616 COVID-19, 6012 lung opacity, 10192 normal, and 1345 viral pneumonia images. The analysis, focusing on loss functions and evaluation metrics, demonstrates distinct performance variations among these models. Notably, multiscale models like MViT and EfficientNet tend towards overfitting. Conversely, our vision transformer model, innovatively fine-tuned (FT) on the encoder blocks, exhibits superior accuracy: 95.79% in four-class, 99.57% in three-class, and similarly high performance in binary classifications, along with a recall of 98.58%, precision of 98.87%, F1 score of 98.73%, specificity of 99.76%, and area under the receiver operating characteristic (ROC) curve (AUC) of 0.9993. The study confirms the vision transformer model’s efficacy through rigorous validation using quantitative metrics and visualization techniques and illustrates its superiority over conventional models. The innovative fine-tuning method applied to vision transformers presents a significant advancement in medical image analysis, offering a promising avenue for improving the accuracy and reliability of COVID-19 diagnosis from chest X-ray images.

Traditional randomized clinical trials are regarded as the gold standard for assessing the efficacy of chemotherapy. However, this procedure has drawbacks such as high cost, time consumption, and limited patient exploration of treatment regimens. We develop a multi-objective optimization-based framework to address these limitations and determine the best chemotherapy dosing and treatment duration. The proposed framework uses patient-specific biological parameters to create a mathematical model of cell population dynamics in the patient’s body. The framework employs evolutionary heuristic search methods (simulated annealing and genetic algorithms) and a prescriptive analytics approach to optimize therapy sessions that transition from treatment to relaxation. We carefully adjust the chemotherapy dose during treatment to reduce tumor cells while preserving host cells (such as effector-immune cells). We strategically time the relaxation sessions to aid recovery, considering the ability of tumors and healthy cells to regenerate. We use a combined optimization method to determine the length of the session and the amount of drug to be administered. We compare quadratic and linear optimal control solvers for drug administration while genetic algorithms and simulated annealing are used to optimize session length. This approach is especially important in limited healthcare resources, ensuring efficient allocation while accurately identifying high-risk patients to optimize resource allocation and utilization.

The malaria’s multidrug-resistant strain in Nigeria is prevalent and it poses a significant challenge for disease elimination. The testing for resistance is available but underutilized. Therefore, we develop a mathematical model incorporating the testing as a control strategy. This allows us to make quantifiable predictions about the effects of testing utilization on the malaria prevalence. By fitting the model to data on malaria and using field data reported in the literature, important parameters associated with the disease dynamics are estimated and calculated. First, we analyze the disease-free state of the malaria model and calculate the baseline control reproduction number. Sensitivity analysis is used to investigate the influence of the parameters in curtailing the disease. Numerical simulations are used to explore the behavior of the model solutions involving testing for resistance of the strain and wild strain malaria. We found that the implementation of testing would (a) prevent the increase of malaria prevalence from 30% to 35%, (b) significantly slow down the replacement of the wild strain by the resistant strain, and (c) avert about 6% of treatment failures. We also found that increasing mosquito death rate or reducing mosquito biting rate, mosquito birth rate, transmission to or from mosquitoes would contribute most significantly to the reduction of malaria prevalence in the community. In conclusion, the treatment failure is a significant component of the community malaria epidemic. Testing for multidrug resistance yields a significant reduction in cases with many implications regarding the containment of malaria in Nigeria.

The primary procedures for breast cancer diagnosis involve the assessment of histopathological slide images by skilled patholo-gists. This procedure is prone to human subjectivity and can lead to diagnostic errors with adverse implications for patient health and welfare. Artificial intelligence-based models have yielded promising results in other medical tasks and offer tools for potentially addressing the shortcomings of traditional medical image analysis. The BreakHis breast cancer dataset suffers from insufficient data for the minority class with an imbalance ratio >0.40, which poses challenges for deep learning models. To avoid performance degradation, researchers have explored a variety of data augmentation schemes to generate adequate samples for analysis. This study designed a Deep Convolutional Neural Network (DCGAN) with specific generator and discriminator architectures to mitigate model instability and generate high-quality synthetic data for the minority class. The balanced dataset was passed to the fine-tuned ResNet50 model for breast tumor detection. The study produced high accuracy in diagnosing benign/malignancy at 40X magnification, outperforming the state-of-art. The results demonstrated that deep learning methods can potentially to support effective screening in clinical practice.

COVID-19 has been a significant threat to many countries worldwide. COVID-19 remains a threat even in the presence of vaccination. The study formulates and analyzes a non-linear deterministic mathematical model to investigate the dynamics of COVID-19 in the presence of vaccination. Numerical results show that increasing the treatment rates with a relatively high vaccination rate might subdue the virus in the population. Also, decreasing the vaccine inefficacy increases the vaccine efficacy, and this may result in a population free of the virus. We further show that increasing the vaccination rate as against the vaccine inefficacy, the effective contact rate for COVID-19 and the modification parameter that accounts for increased infectiousness for COVID-19, the virus responsible for COVID-19 can be eradicated from the population. The sensitivity analysis results deduce that hidden factors are driving the model dynamics. These hidden factors must be given special attention and minimized. These factors includes the incubation periods for vaccinated and unvaccinated individuals, the fractions for vaccinated and unvaccinated individuals, and the transition rates for vaccinated and unvaccinated individuals

The Coronavirus Disease 2019 (COVID-19) pandemic has swept almost all parts of the world. With the increasing number of COVID-19 cases worldwide, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has mutated into various variants. Given the increasingly dangerous conditions of the pandemic, it is crucial to predict the number of COVID-19 cases. Deep Learning and Long Short-Term Memory (LSTM) have predicted disease progress with reasonable accuracy and small errors. LSTM training is used to predict confirmed cases of COVID-19 based on variants identified using the European Centre for Disease Prevention and Control (ECDC) COVID-19 dataset containing confirmed cases identified from 30 European countries. Tests were conducted using the LSTM and Bidirectional LSTM (BiLSTM) models with the addition of Recurrent Neural Network (RNN) as comparisons on hidden size and layer size. The obtained result showed that in testing hidden sizes 25, 50, 75, and 100, the RNN model provided better results, with the minimum Mean Squared Error (MSE) value of 0.01 and the Root Mean Square Error (RMSE) value of 0.012 for B.1.427/B.1.429 variant with a hidden size of 100. Further testing layer sizes 2, 3, 4, and 5 shows that the BiLSTM model provided better results, with a minimum MSE value of 0.01 and an RMSE of 0.01 for the B.1.427/B.1.429 variant with a hidden size of 100 and layer size of 2.