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.

Introduction: Emergency department (ED) health care providers had one of the highest incidences of burnout just before the pandemic. Personality type influences the way we communicate and relate to others, as well as our reaction to stress. In evaluating the causes of burnout, we take into account several factors, the most important of which being the conditions of the professional environment, interpersonal relationships and personality traits. This study aims to identify and analyze the relationships between personality traits and burnout syndrome in medical staff in the Emergency Department. Material and Methods: A descriptive, multicenter cross-sectional study project was used to collect data from 60 physicians and nurses from the Emergency Department of the County Emergency Clinical Hospital Galati. The tools used, distributed to participants to collect socio-demographic details, included the Eysenck Personality Inventory (EPI), and a pilot questionnaire. The questionnaire consisted of 11 questions, concerning exhaustion level, and focused on stress-generating elements and emotional management in the Emergency Department during the pandemic. Results: A large percentage of doctors in ED (45.8%) (n = 24) indicated a high level of exhaustion, while only 30.6% (n = 36) of nurses indicated a high level of exhaustion. Physicians displayed a higher level of stress than nurses, with 66.6% (n = 24) of physicians indicating an increased level of stress, compared to only 36.1% (n = 36) of nurses. A large percentage of nurses indicated a high level of depression (36.1%), whereas only 25% of Emergency Department physicians indicated a high level of depression. Discussion: The individuals with a melancholic and choleric temperament, as rated by the EPI, appeared more susceptible to developing burnout syndrome. The indication of instability or neuroticism appears to enhance the occurrence of burnout syndrome. There was also a correlation between extraversion and a high level of burnout, with extraverted individuals more easily reaching a high level of burnout. Conclusion: The results indicate a greater occurrence of exhaustion and stress in Emergency Department physicians compared to nurses in the Emergency Department. From the results, we can propose that there is a relationship between the personality traits as measured by the EPI and the occurrence of burnout syndrome. These findings may help to understand the vulnerability of medical staff to burnout, and to pre-emptively support them to deal effectively with stressors in the work environment.

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.

Introduction: Emotional affectivity can be described under two general dimensions: positive affectivity and negative affectivity. It is often assessed by means of questionnaires completed retrospectively by the subjects. The most common scales used are PANAS, DES, and PANA-X. All these scales are based on the concept of two dimensions, negative and positive affective emotions, respectively. Both dimensions mentioned, positive and negative affectivity, can be associated with a bipolar dimensional entity called pleasant-unpleasant. Specifically, a high level of positive emotions and a low level of negative emotions are associated with pleasant/positive feelings (happiness, well-being, satisfaction, cheerfulness, etc.), while a low level of positive emotions and a high level of negative emotions are associated with negative feelings (sadness, anger, nervousness, depression, anxiety, etc.). Materials and Methods: This study is an observational and cross-sectional study. The elements that led to the production of the final database were collected by means of a questionnaire, which included 43 items, 39 of which were specific questions found in the affective distress profile. The questionnaire was administered to 145 patients who had suffered polytrauma and were admitted to the Emergency Hospital of Galati in October 2022. The final centralizing tables included the characteristics of 145 patients, aged between 14 and 64 years. Results: The aim of this study is to identify the level of emotional distress in patients who have suffered polytrauma, so the following scores were evaluated, obtained after the application of PDA: STD, ENF, END. The total distress score was obtained by summing all the negative items found in the PDA questionnaire. Conclusion: Men show a high level of emotional distress compared to women. Polytrauma has a negative effect on patients’ emotional status, with a worrying prevalence of negative functional and dysfunctional emotions. The level of distress in polytrauma patients is high.