Cardiovascular disease (CVD) is a significant global health concern and the leading cause of death in many countries. Early detection and diagnosis of CVD can significantly reduce the risk of complications and mortality. Machine learning methods, particularly classification algorithms, have demonstrated their potential to accurately predict the risk of cardiovascular disease (CVD) by analyzing patient data. This study evaluates seven binary classification algorithms, including Random Forests, Logistic Regression, Naive Bayes, K-Nearest Neighbors (kNN), Support Vector Machines, Gradient Boosting, and Artificial Neural Networks, to understand their effectiveness in predicting CVD. Advanced preprocessing techniques, such as SMOTE–ENN for addressing class imbalance and hyperparameter optimization through Grid Search Cross-Validation, were applied to enhance the reliability and performance of these models. Standard evaluation metrics, including accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (ROC-AUC), were used to assess predictive capabilities. The results show that kNN achieved the highest accuracy (99%) and AUC (0.99), surpassing traditional models like Logistic Regression and Gradient Boosting. The study examines the challenges encountered when working with datasets related to cardiovascular diseases, such as class imbalance and feature selection. It demonstrates how addressing these issues enhances the reliability and applicability of predictive models. These findings emphasize the potential of kNN as a reliable tool for early CVD prediction, offering significant improvements over previous studies. This research highlights the value of advanced machine learning techniques in healthcare, addressing key challenges and laying a foundation for future studies aimed at improving predictive models for CVD prevention.

Burnout syndrome is caused by a number of factors, including personal, organizational, and professional problems. The purpose of this study was to investigate burnout and satisfaction levels among emergency department staff in the context of professional activity issues. We studied a sample of 184 participants working in the emergency department. Participants signed an informed consent form, completed a socio-demographic questionnaire and the MBI-HSS (MP) questionnaire to assess burnout, a JSS (Job Satisfaction Survey) to assess their professional satisfaction, and the AWS (AWS of work survey) questionnaire on work aspects. The questionnaires were completed between November 2022 and March 2023. The study group was aged between 24–64 years old, most of the subjects being female. The study found that 30.2% of emergency medical staff are at high risk of burnout. Emotional exhaustion is indirectly proportional to workload, interpersonal relationships, and rewards. Emotional exhaustion is a direct result of work experience. While participants expressed ambivalence and dissatisfaction with the work environment, they were satisfied with the nature of their work. This study found that job-related factors such as social support and feedback are significant predictors of employee well-being and reducing the risk of burnout. Emotional exhaustion was negatively correlated with job satisfaction, while personal accomplishment was positively correlated with job satisfaction. Depersonalization was associated with job dissatisfaction with operating conditions and coworkers. The study also identified differences in burnout and related constructs among healthcare professionals, with resident physicians reporting higher levels of personal accomplishments and paramedics reporting relatively low levels of emotional exhaustion. The findings suggest that tailored interventions addressing job demands and resources are critical in improving employee well-being and reducing burnout.

Background and Objectives: Burnout syndrome is caused by a number of factors, including personal, organizational, and professional problems. Data from the literature reported a strong relationship between burnout syndrome and job satisfaction among emergency medical personnel. Materials and Methods: We studied a sample of 80 nurses working in the Emergency Department of Emergency Clinical County Hospital “Sfântul Apostol Andrei” of Galati Romania. Participants signed an informed consent and then completed a socio-demographic questionnaire and the MBI-HSS (Maslach Burnout Inventory–Human Services Survey) questionnaire to assess the level of burnout and JSS (Job Satisfaction Survey) to assess the level of professional satisfaction. The aim of this study was to measure the levels of burnout and satisfaction among nurses in the Emergency Department as well as the relationship between these two variables and a group of selected socio-demographic characteristics. Results: This study found that 36.25% of nurses reported a high level of burnout. Additionally, emotional exhaustion is directly proportional to professional experience and age. Participants also reported a sense of ambivalence and satisfaction with the workplace, but were satisfied with the nature of their work. Conclusions: The results of this study suggest the relationship between variables and this could be used to implement psychoactive intervention strategies at both individual and organizational levels, which could lead to a decrease in burnout levels. Burnout is a predictor of job satisfaction for Emergency Department nurses. Personal achievement was relatively commensurate with the nature of the work. Additionally, the increase in burnout among nurses is directly proportional to the nature of the work.

Deep venous thrombosis is a critical medical condition that occurs when a blood clot forms in a deep vein, usually in the legs, and can lead to life-threatening complications such as pulmonary embolism if not detected early. Hospitalized patients, especially those with immobility or post-surgical recovery, are at higher risk of developing deep venous thrombosis, making early prediction and intervention vital for preventing severe outcomes. In this study, we evaluated the following eight machine learning models to predict deep venous thrombosis risk: logistic regression, random forest, XGBoost, artificial neural networks, k-nearest neighbors, gradient boosting, CatBoost, and LightGBM. These models were rigorously tested using key metrics, including accuracy, precision, recall, F1-score, specificity, and receiver operating characteristic curve, to determine their effectiveness in clinical prediction. Logistic regression emerged as the top-performing model, delivering high accuracy and an outstanding receiver operating characteristic curve score, which reflects its strong ability to distinguish between patients with and without deep venous thrombosis. Most importantly, the model’s high recall underscores its ability to identify nearly all true deep venous thrombosis cases, significantly reducing the risk of false negatives—a critical concern in clinical settings, where delayed or missed diagnoses can result in life-threatening complications. Although models such as random forest and eXtreme Gradient Boosting also demonstrated competitive performances, logistic regression proved the most reliable across all metrics. These results suggest that machine learning models, particularly logistic regression, have great potential for early deep venous thrombosis detection, enabling timely clinical interventions and improved patient outcomes.

Most cases of superior vena cava syndrome are easily diagnosed bya clinical examination alone, but several diagnostic tests and procedures can be helpful. When a patient presentswith a suspected diagnosis of superior vena cava syndrome, the first step is to obtain an imaging study that confirms the diagnosis and aids treatment decisions. Magnetic resonance imaging, contrast-enhanced CT scanning, radionuclide flow studies and traditional venography are all appropriate techniques. Still, the CT scan is the most readily available technology in most centers. The CT scan and magnetic resonance imaging also provide information on possible etiologies and can therefore direct the approach towards a tissue diagnosis.

Background and objectives: This study aimed to assess the impact and predicted outcomes of patients with multiple trauma by identifying the prevalence of trauma sustained and associated complications. Materials and Methods: This retrospective cohort study focused on individual characteristics of patients with multiple trauma admitted to our County Emergency Hospital. The final table centralized the characteristics of 352 subjects aged between 3 and 93 years who presented with multiple trauma from 2015 to 2021. Inclusion criteria for this study were the presence of multiple trauma, intervention times, mentioned subjects’ ages, and types of multiple trauma. Results: Patients with multiple trauma face an increased risk of mortality due to the underlying pathophysiological response. Factors that can influence the outcomes of multiple-trauma patients include the severity of the initial injury, the number of injuries sustained, and the location of injuries. Conclusion: The first 60 min after trauma, known as the “golden hour,” is crucial in determining patient outcomes. Injuries to the head, neck, and spine are particularly serious and can result in life-threatening complications.