Improving emergency departments: simulation-based optimization of patients waiting time and the number of staff present in a hospital

The emergency department (ED), operating around the clock every day of the year, serves a diverse range of patients with varying medical conditions, making it the vital core of a hospital. Consequently, optimizing and simulating the ED's processes becomes essential to enhance the quality of care provided. This study offers a case analysis employing simulation to assess patient flows in a hospital's emergency department. Our objective is to evaluate the impacts of system enhancements within the ED. This model aims to measure patients' time from their ED entry, determine daily patient numbers, and calculate the overall patient movement time within the department. If the patient's condition is serious, he will be presented immediately to the doctor without waiting. A doctor will be added to the unit if the number of patients exceeds the standard limit.</p

Sand cat swarm optimizer with CatBoost for Sarcoidosis diagnosis

In the last few years, machine learning has increased in popularity across many disciplines. This paper aims to comprehensively analyze the CatBoost classification algorithm in the context of Sarcoidosis. Analysis was undertaken to evaluate the performance of the CatBoost classification algorithm in comparison to other classifiers. The CatBoost algorithm outperformed other classifiers exploited in this study to identify and differentiate Sarcoidosis. Previous scholarly works ignored missing data observations or filled them with mean values; on the other hand, this study has uncovered that the SIL-2R feature holds significant importance in predicting the occurrence of Sarcoidosis, which improved the selection of treatment and its efficacy. A comprehensive understanding of Sarcoidosis is essential to accurately differentiating symptoms associated with this illness from those associated with other conditions. It is strongly recommended that the CatBoost algorithm be used for sarcoidosis prediction.</p

Assessment of mixing efficiency in a planar passive micromixer With t-shaped configuration

Microfluidic devices have garnered considerable interest owing to their prospective utilization in diverse domains, encompassing chemical synthesis, biological analysis, and medicinal research. Micromixers are critical in adequate fluid mixing at a microscale within the array of devices under consideration. This study aims to offer a comprehensive analysis of the efficacy of the T-shaped micromixer configuration in scenarios that necessitate accurate and expeditious mixing. This study examines the performance of a T micromixer through simulation and analysis. The findings demonstrate that T micromixers exhibit some drawbacks that result in suboptimal mixing efficiency. The attainment of a desirable level of mixing efficiency can be accomplished by utilizing splitting-recombination and chaotic advection mechanisms. The study's outcomes indicate that the T micromixer demonstrates its maximum mixing effectiveness, roughly 60% when the Reynolds number (Re) is at or below 0.5. Nevertheless, it has been observed that the T micromixer encounters a decrease in mixing effectiveness as the Reynolds number escalates within the range of 0.5 to 15.</p

Evaluation of the mixing performance in a planar passive micromixer with T micromixer with square chamber mixing units (SAR)

Microscale mixing methods are crucial in various disciplines, encompassing chemical reactions and biological investigations. The present study used simulation methodologies to investigate the operational efficiency of splitting recombination (SAR) micromixers. The study demonstrates that SAR micromixers offer a notable advantage in enhancing mixing efficiency. The advantage above is a consequence of the effective combination of splitting-recombination and chaotic advection processes within the micromixer architecture. An in-depth analysis of the micromixer's behavior demonstrates that its performance is supported by intricate fluid dynamics, which provide remarkably high mixing efficiency. It is worth noting that the micromixer exhibits its maximum mixing efficiency, which is roughly 99% when the Reynolds number (Re) is at or below 0.5. Nevertheless, it is seen that as the Reynolds number grows, there is a steady decrease in mixing efficiency. At a Reynolds number of 70, the measurement of mixing efficiency yields a value of 75%. However, when the Reynolds number is further increased to a range of 90-100, the efficiency decreases to its lowest value of approximately 60%. The results above highlight the exceptional mixing ability of the SAR micromixer, hence stressing its potential for various applications that demand improved mixing capabilities. The results emphasize the promise of SAR micromixers as a reliable solution for complex mixing processes in many applications, providing valuable insights that may contribute to future developments in microscale mixing technologies.</p

Evaluating the mixing performance in a planar passive micromixer with t-shape and SAR mixing chambers: comparative study

In Microfluidic devices have gained significant interest in various fields, including biomedical diagnostics, environmental preservation, animal epidemic avoidance, and food safety regulation. Micromixing phenomena are crucial for these devices' functionality, as they accurately and efficiently manipulate fluids within microchannels. The process aims to blend samples accurately and swiftly within these scaled-down devices, governed by the promotion of dispersion among distinct fluid species or particles. Advancements in passive and active micromixers have led to innovative designs incorporating diverse processes to enhance mixing efficiency. Examples include two-dimensional impediments, controlled imbalanced collisions, and complex configurations like spiral and convergence-divergence structures. Active micromixers use external cues to initiate and regulate mixing processes, including thermal, magnetic, sound, pressure, and electrical fields. The trajectory of micromixing technologies is significantly influenced by current developments in microfluidics. One notable advancement is the incorporation of micromixers into 3D printing methodologies, facilitating the development of adaptable microfluidic systems. Additionally, the incorporation of microfluidic principles into paper-based channels creates opportunities for the development of cost-effective and portable diagnostic devices. The process of micro-mixing is critical in boosting the functionalities of these devices.</p

Machine learning model based on Gary-level co-occurrence matrix for chest Sarcoidosis diagnosis

Sarcoidosis is often misdiagnosed and mistreated due to the limitations of radiological presentations. With the recent emergence of COVID-19, doctors face challenges distinguishing between the symptoms of these two diseases. As a result, people are adapting to new practices such as working from home, wearing masks, and using disinfectants. The similarity in symptoms between sarcoidosis and COVID-19 has made it difficult to differentiate between the two conditions, potentially impacting patient outcomes. The diagnostic process for distinguishing between them is time-consuming, labor-intensive, and costly. Researchers and medical practitioners have gained significant attention to computer-aided detection (CAD) systems for sarcoidosis using radiological images to address this issue. This study uses machine learning classifiers, ensembles, and features such as Gray-Level Co-occurrence Matrix (GLCM) and histogram analysis to identify lung sarcoidosis infection from chest X-ray images. The proposed method extracts statistical texture features from X-ray images by calculating a GLCM for each image using various stride combinations. These GLCM features are then used to train the machine learning classifiers and ensembles. The research focuses on multi-class classification, categorizing X-ray images into three classes: sarcoidosis-affected, COVID-19-affected, and regular lungs, as well as binary classification, distinguishing sarcoid-affected cases from others. The proposed method, known for its simplicity and computational efficiency, demonstrates significant accuracy in identifying sarcoidosis and COVID-19 from chest X-ray images.</p

Prediction of wear rates of UHMWPE bearing in hip joint prosthesis with support vector model and grey wolf optimization

One of the greatest challenges in joint arthroplasty is to enhance the wear resistance of ultrahigh molecular weight polyethylene (UHMWPE), which is one of the most successful polymers as acetabular bearings for total hip joint prosthesis. In order to improve UHMWPE wear rates, it is necessary to develop efficient methods to predict its wear rates in various conditions and therefore help in improving its wear resistance, mechanical properties, and increasing its life span inside the body. This article presents a support vector machine using a grey wolf optimizer (SVM-GWO) hybrid regression model to predict the wear rates of UHMWPE based on published polyethylene data from pin on disc (PoD) wear experiments typically performed in the field of prosthetic hip implants. The dataset was an aggregate of 29 different PoD UHMWPE datasets collected from Google Scholar and PubMed databases, and it consisted of 129 data points. Shapley additive explanations (SHAP) values were used to interpret the presented model to identify the most important and decisive parameters that affect the wear rates of UHMWPE and, therefore, predict its wear behavior inside the body under different conditions. The results revealed that radiation doses had the highest impact on the model’s prediction, where high values of radiation doses had a negative impact on the model output. The pronounced effect of irradiation doses and surface roughness on the wear rates of polyethylene was clear in the results when average disc surface roughness (Ra) values were below 0.05 μm, and irradiation doses were above 95 kGy produced 0 mg/MC wear rate. The proposed model proved to be a reliable and robust model for the prediction of wear rates and prioritizing factors that most significantly affect its wear rates. The proposed model can help material engineers to further design polyethylene acetabular linings via improving the wear resistance and minimizing the necessity for wear experiments

Gradient Boosting Machine Based on PSO for prediction of Leukemia after a Breast Cancer Diagnosis

The purpose of this study is to develop an accurate risk predictive model for Chronic Myeloid Leukemia (CML) after an early diagnosis of Breast Cancer (BC). Gradient Boosting Machine (GBM) classification algorithm has been applied to the SEER breast cancer dataset for females diagnosed with BC from 2010 to 2016. A practical Swarm optimizer (PSO) was utilized to optimize the GBM algorithm's hyperparameters to find the SEER dataset's best attributes. Nine attributes were carefully selected to study the growth of CML after a lag time of 6 months following BC's diagnosis. The results revealed that the predictive model could classify patients with breast cancer only and patients with breast cancer with Leukemia by an achieved Accuracy, Sensitivity, and Specificity rates of 98.5 %, 99 %, 97.85 %, respectively. To verify the performance of the proposed algorithm, the accuracy of the suggested GBM classifier model was compared with another state-of-the-art model classifiers KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and RF (Random Forest), which are commonly applied algorithms in most of the existing literature. The results also proved the superior ability of the implemented GBM model Classifier in the classification of breast cancer disease and prediction of patients having Leukemia developed after having breast cancer. These results are promising as they show the integral role of the GBM classifier to classify and predict the tumor with high accuracy and efficiency, which will further help in better cancer diagnosis and treatment of the disease

Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach

The sudden increase in patients with severe COVID-19 has obliged doctors to make admissions to intensive care units (ICUs) in health care practices where capacity is exceeded by the demand. To help with difficult triage decisions, we proposed an integration system Xtreme Gradient Boosting (XGBoost) classifier and Analytic Hierarchy Process (AHP) to assist health authorities in identifying patients’ priorities to be admitted into ICUs according to the findings of the biological laboratory investigation for patients with COVID-19. The Xtreme Gradient Boosting (XGBoost) classifier was used to decide whether or not they should admit patients into ICUs, before applying them to an AHP for admissions’ priority ranking for ICUs. The 38 commonly used clinical variables were considered and their contributions were determined by the Shapley’s Additive explanations (SHAP) approach. In this research, five types of classifier algorithms were compared: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), Random Forest (RF), and Artificial Neural Network (ANN), to evaluate the XGBoost performance, while the AHP system compared its results with a committee formed from experienced clinicians. The proposed (XGBoost) classifier achieved a high prediction accuracy as it could discriminate between patients with COVID-19 who need ICU admission and those who do not with accuracy, sensitivity, and specificity rates of 97%, 96%, and 96% respectively, while the AHP system results were close to experienced clinicians’ decisions for determining the priority of patients that need to be admitted to the ICU. Eventually, medical sectors can use the suggested framework to classify patients with COVID-19 who require ICU admission and prioritize them based on integrated AHP methodologies