Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

AGV Malfunction Prediction in a 5G Network Using Novel Deep Learning Techniques

Una aplicación prometedora de las redes 5G en la Industria 4.0 es el uso de Vehículos de Guiado Automático avanzados (Automated Guided Vehicles, AGV). En este Trabajo de Fin de Máster, proponemos una novedosa arquitectura de control de AGVs que explota las capacidades únicas que brindan las redes 5G, como la alta velocidad de datos, la baja latencia y las comunicaciones ultra fiables, para abrir las posibilidades de despliegue de los AGV en una gama más amplia de aplicaciones industriales y comerciales. En esta arquitectura, el AGV se controla de forma remota mediante un Controlador Lógico Programable (Programmable Logic Controller, PLC) virtual, que se despliega en una plataforma de computación en el borde de acceso múltiple (Multi-access Edge Computing, MEC) y se conecta al AGV a través de un enlace de radio 5G para satisfacer los requisitos de control determinista y de baja latencia del entorno industrial. En este escenario, aprovechamos las técnicas avanzadas de Deep Learning (DL) basadas en ensembles de N-BEATS para construir modelos predictivos que pueden anticipar con 20 segundos de antelación la desviación de la trayectoria del AGV con respecto a una cinta magnética que traza el circuito a seguir en la fábrica, incluso cuando aparecen perturbaciones en la red. De este modo, las maniobras correctivas, como la detención del AGV, pueden realizarse con antelación para evitar situaciones potencialmente perjudiciales. Para satisfacer este objetivo, proponemos una aplicación innovadora del modelado secuencia a secuencia de la desviación del AGV que permite una forma flexible de adaptar el horizonte de previsión a las necesidades actuales del operador del AGV sin requerir el reentrenamiento del modelo y sin perder precisión, a la vez que proporciona una información más rica del comportamiento futuro del AGV que puede explotarse utilizando métodos estadísticos para proporcionar una detección de fallos del AGV más robusta o mejorar los algoritmos de control de seguimiento del AGV actuales. Por otra parte, ampliamos la arquitectura N-BEATS para considerar variables exógenas con información relevante (estadísticas de conexión PLC-AGV y oscilaciones de la guía del AGV a lo largo de la cinta que traza el circuito) conjuntamente con variables endógenas (desviación del AGV de la cinta que traza el circuito). La solución propuesta se evaluó exhaustivamente a través de escenarios realistas en un entorno de fábrica real con conectividad 5G (5TONIC Open Innovation Laboratory) y se comparó con arquitecturas de aprendizaje profundo (LSTM), técnicas de aprendizaje automático (Random Forest) y métodos estadísticos tradicionales (ARIMA). Demostramos que el mal funcionamiento de los AGVs puede prevenirse eficazmente mediante el uso de ensembles de modelos de nuestra arquitectura N-BEATS extendida que superan claramente a los otros métodos propuestos. Además, los ensembles de modelos N-BEATS muestran un rendimiento consistente con respecto al tamaño de la ventana temporal, lo que puede favorecer los despliegues rápidos en tiempo real al no requerir un ajuste fino de este hiperparámetro. Por último, se llevó a cabo un cuidadoso análisis de un despliegue en tiempo real de nuestra solución, incluyendo escenarios de reentrenamiento que podrían ser provocados por la aparición de cambios imprevistos en la distribución estadística del error de guiado provocados principalmente por cambios en el entorno de la fábrica o el desgaste de los componentes físicos del AGV. Como trabajo futuro, sugerimos ampliar los experimentos para incluir tamaños de ventana mayores, ya que pueden proporcionar una mejora potencial del rendimiento. Además, otra línea de investigación interesante consiste en examinar y evaluar métodos para detectar las desviaciones de datos que pueden producirse a lo largo del tiempo durante las actividades de producción para activar automáticamente el reentrenamiento del modelo de forma online utilizando los datos recogidos a medida que la flota de AGV realiza sus actividades operativas. Abstract: One promising application of 5G networks in Industry 4.0 is the use of advanced Automated Guided Vehicles (AGVs). In this Master’s Thesis, we propose a novel AGV control architecture that exploits the unique capabilities provided by 5G networks, such as high data rate, low latency, and ultra-reliable communications, to open up the possibilities for deployment of AGVs in a wider range of industrial and commercial applications. In this architecture, the AGV is controlled remotely by a virtual Programmable Logic Controller (PLC), which is deployed on a Multi-access Edge Computing (MEC) platform and connected to the AGV via a 5G radio link to satisfy the deterministic and low-latency control requirements of the industrial environment. In this scenario, we leverage advanced Deep Learning (DL) techniques based on ensembles of N-BEATS to build predictive models that can anticipate 20 seconds in advance the deviation of the AGV’s trajectory with respect to a guiding tape on the factory floor, even when network perturbations appear. Therefore, corrective maneuvers, such as stopping the AGV, can be performed in advance to avoid potentially harmful situations. To meet this objective, we propose an innovative application of sequence-to-sequence modeling of AGV deviation that allows a flexible way to adapt the forecast horizon to the current needs of the AGV operator without requiring model retraining and without losing performance, while providing richer information of future AGV behavior that can be exploited using statistical methods to further improve the robustness of AGV malfunction detection or enhance state-of-the-art AGV tracking control algorithms. On the other hand, we extended the N-BEATS architecture to consider exogenous variables with relevant information (PLC-AGV connection statistics and AGV guide oscillations along the guiding tape) jointly with endogenous variables (AGV deviation from the guiding tape). The proposed solution was thoroughly evaluated through realistic scenarios in a real factory environment with 5G connectivity (5TONIC Open Innovation Laboratory) and compared against common deep learning architectures (LSTM), machine learning techniques (Random Forest) and statistical methods (ARIMA). We demonstrate that the malfunctioning of AGVs can be effectively prevented by using ensembles of our extended N-BEATS architecture that clearly outperform the other methods. Furthermore, the N-BEATS ensembles exhibit consistent performance with respect to the time window size, which can favor rapid real-time deployments by not requiring fine-tuning of this variable. Finally, a careful analysis of a real-time deployment of our solution was conducted, including retraining scenarios that could be triggered by the appearance of data drift problems caused mainly by changes in the factory environment or by physical wear and tear of the AGV. As future work, we suggest extending the experiments to include larger window sizes, as they may provide a potential performance improvement. In addition, another interesting line of research is to examine and evaluate methods to detect data drifts that may occur over time during production activities to automatically trigger model retraining in an online fashion using data collected as the AGV fleet performs its operational activities

B5GEMINI: AI-Driven Network Digital Twin

Network Digital Twin (NDT) is a new technology that builds on the concept of Digital Twins (DT) to create a virtual representation of the physical objects of a telecommunications network. NDT bridges physical and virtual spaces to enable coordination and synchronization of physical parts while eliminating the need to directly interact with them. There is broad consensus that Artificial Intelligence (AI) and Machine Learning (ML) are among the key enablers to this technology. In this work, we present B5GEMINI, which is an NDT for 5G and beyond networks that makes an extensive use of AI and ML. First, we present the infrastructural and architectural components that support B5GEMINI. Next, we explore four paradigmatic applications where AI/ML can leverage B5GEMINI for building new AI-powered applications. In addition, we identify the main components of the AI ecosystem of B5GEMINI, outlining emerging research trends and identifying the open challenges that must be solved along the way. Finally, we present two relevant use cases in the application of NDTs with an extensive use of ML. The first use case lays in the cybersecurity domain and proposes the use of B5GEMINI to facilitate the design of ML-based attack detectors and the second addresses the design of energy efficient ML components and introduces the modular development of NDTs adopting the Digital Map concept as a novelty

Transformers for Multi-Horizon Forecasting in an Industry 4.0 Use Case

Recently, a novel approach in the field of Industry 4.0 factory operations was proposed for a new generation of automated guided vehicles (AGVs) that are connected to a virtualized programmable logic controller (PLC) via a 5G multi-access edge-computing (MEC) platform to enable remote control. However, this approach faces a critical challenge as the 5G network may encounter communication disruptions that can lead to AGV deviations and, with this, potential safety risks and workplace issues. To mitigate this problem, several works have proposed the use of fixed-horizon forecasting techniques based on deep-learning models that can anticipate AGV trajectory deviations and take corrective maneuvers accordingly. However, these methods have limited prediction flexibility for the AGV operator and are not robust against network instability. To address this limitation, this study proposes a novel approach based on multi-horizon forecasting techniques to predict the deviation of remotely controlled AGVs. As its primary contribution, the work presents two new versions of the state-of-the-art transformer architecture that are well-suited to the multi-horizon prediction problem. We conduct a comprehensive comparison between the proposed models and traditional deep-learning models, such as the long short-term memory (LSTM) neural network, to evaluate the performance and capabilities of the proposed models in relation to traditional deep-learning architectures. The results indicate that (i) the transformer-based models outperform LSTM in both multi-horizon and fixed-horizon scenarios, (ii) the prediction accuracy at a specific time-step of the best multi-horizon forecasting model is very close to that obtained by the best fixed-horizon forecasting model at the same step, (iii) models that use a time-sequence structure in their inputs tend to perform better in multi-horizon scenarios compared to their fixed horizon counterparts and other multi-horizon models that do not consider a time topology in their inputs, and (iv) our experiments showed that the proposed models can perform inference within the required time constraints for real-time decision making

Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range

Cybercrime has become more pervasive and sophisticated over the years. Cyber ranges have emerged as a solution to keep pace with the rapid evolution of cybersecurity threats and attacks. Cyber ranges have evolved to virtual environments that allow various IT and network infrastructures to be simulated to conduct cybersecurity exercises in a secure, flexible, and scalable manner. With these training environments, organizations or individuals can increase their preparedness and proficiency in cybersecurity-related tasks while helping to maintain a high level of situational awareness. SPIDER is an innovative cyber range as a Service (CRaaS) platform for 5G networks that offer infrastructure emulation, training, and decision support for cybersecurity-related tasks. In this paper, we present the integration in SPIDER of defensive exercises based on the utilization of machine learning models as key components of attack detectors. Two recently appeared network attacks, cryptomining using botnets of compromised devices and vulnerability exploit of the DoH protocol (DNS over HTTP), are used as the support use cases for the proposed exercises in order to exemplify the way in which other attacks and the corresponding ML-based detectors can be integrated into SPIDER defensive exercises. The two attacks were emulated, respectively, to appear in the control and data planes of a 5G network. The exercises use realistic 5G network traffic generated in a new environment based on a fully virtualized 5G network. We provide an in-depth explanation of the integration and deployment of these exercises and a complete walkthrough of them and their results. The machine learning models that act as attack detectors are deployed using container technology and standard interfaces in a new component called Smart Traffic Analyzer (STA). We propose a solution to integrate STAs in a standardized way in SPIDER for the use of trainees in exercises. Finally, this work proposes the application of Generative Adversarial Networks (GANs) to obtain on-demand synthetic flow-based network traffic that can be seamlessly integrated into SPIDER exercises to be used instead of real traffic and attacks