Cardiovascular diseases state as one of the greatest risks of death for the general population. Late detection in heart diseases highly conditions the chances of survival for patients. Age, sex, cholesterol level, sugar level, heart rate, among other factors, are known to have an influence on life-threatening heart problems, but, due to the high amount of variables, it is often difficult for an expert to evaluate each patient taking this information into account. In this manuscript, the authors propose using deep learning methods, combined with feature augmentation techniques for evaluating whether patients are at risk of suffering cardiovascular disease. The results of the proposed methods outperform other state of the art methods by 4.4%, leading to a precision of a 90%, which presents a significant improvement, even more so when it comes to an affliction that affects a large population.

The large number of sensors and actuators that make up the Internet of Things obliges these systems to use diverse technologies and protocols. This means that IoT networks are more heterogeneous than traditional networks. This gives rise to new challenges in cybersecurity to protect these systems and devices which are characterized by being connected continuously to the Internet. Intrusion detection systems (IDS) are used to protect IoT systems from the various anomalies and attacks at the network level. Intrusion Detection Systems (IDS) can be improved through machine learning techniques. Our work focuses on creating classification models that can feed an IDS using a dataset containing frames under attacks of an IoT system that uses the MQTT protocol. We have addressed two types of method for classifying the attacks, ensemble methods and deep learning models, more specifically recurrent networks with very satisfactory results.

The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.

Aim: To describe the use of artificial intelligence (AI) by nurse managers to enhance management, leadership, and healthcare outcomes.Background: AI represents a significant transformation in healthcare management by enhancing decision‐making, communication, and resource optimization. However, the integration and strategic application of AI in nursing management are underexplored, particularly regarding its impact on leadership roles and healthcare delivery.Methods: Methodological guidelines described by PRISMA were followed, and quality was assessed using the Joanna Briggs Institute (JBI) methodology. The databases searched included the Web of Science, Scopus, CINAHLi, and PubMed. The review included quantitative, qualitative, and mixed‐method studies published between January 2015 and April 2024.Results: Fourteen studies were selected for the review. The key findings indicate that AI technologies facilitate better resource management, risk assessment, and decision‐making. AI also supports nurse managers in leading changes, enhancing communication, and optimizing administrative tasks.Conclusion: AI has been progressively integrated into nursing management, demonstrating significant benefits in operational efficiency, decision support, and leadership enhancement. However, challenges, such as resistance to technological change and ethical complexities, need to be addressed.Implications for Nursing Management: Specific training programs for nurse managers are essential to optimize the integration of AI. Such programs should focus on the management of AI applications and data analyses. In addition, creating interdisciplinary groups involving nurse managers, AI developers, and nursing staff is crucial for tailoring AI solutions to meet the unique needs of healthcare settings.