Interoperability in health information systems is increasingly a requirement rather than an option. Standards and technologies, such as multi-agent systems, have proven to be powerful tools in interoperability issues. In the last few years, the authors have worked on developing the Agency for Integration, Diffusion and Archive of Medical Information (AIDA), which is an intelligent, agent-based platform to ensure interoperability in healthcare units. It is increasingly important to ensure the high availability and reliability of systems. The functions provided by the systems that treat interoperability cannot fail. This paper shows the importance of monitoring and controlling intelligent agents as a tool to anticipate problems in health information systems. The interaction between humans and agents through an interface that allows the user to create new agents easily and to monitor their activities in real time is also an important feature, as health systems evolve by adopting more features and solving new problems. A module was installed in Centro Hospitalar do Porto, increasing the functionality and the overall usability of AIDA.

The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one.

Standardization is a key element in the effective use of lean manufacturing methodologies and tools for achieving process sustainability. Their combination is conducive to eliminating waste and improving the efficiency of production processes and guarantees the company that employees use the most efficient tools and do not waste time on unnecessary activities. These activities can be further improved by using digital solutions, in accordance with the concept of Industry 4.0. Therefore, the authors have developed the e-Lean system, whose task is to digitize selected lean manufacturing tools. The subject of this work is analysis of the functionality and effectiveness of the essential part of the e-Lean system in the form of specialized TPM (Total Productive Maintenance) software as an application. During implementation in a construction production company, the TPM application was tested by lean manufacturing and maintenance specialists. The research consisted of assessing the functionality and efficiency of processes in relation to conventional TPM solutions. Additional functionalities of the e-Lean system have been confirmed, such as systemic approval of machinery inspection, which requires passing all necessary steps at individual inspection points, direct access for supervisors to the results of inspection activities and their status, direct and easy access to photographic documentation of machines added during inspection both in optimization of working time and its course (e.g., the optimal number of steps taken by the employee during the inspection), as well as an efficient system of motivating employees (collecting points). The improvement in the effectiveness of processes was determined by measuring the control times for three control points (polymerization furnace, packing area, and defibering machines). The average control time was reduced from 16,200 to 13,923 s. Thus, thanks to the use of the application, it was found that the efficiency of using the TPM tool was increased by approx. 15% compared to previously used non-digital solutions.

In Maternity Care, a quick decision has to be made about the most suitable delivery type for the current patient. Guidelines are followed by physicians to support that decision; however, those practice recommendations are limited and underused. In the last years, caesarean delivery has been pursued in over 28% of pregnancies, and other operative techniques regarding specific problems have also been excessively employed. This study identifies obstetric and pregnancy factors that can be used to predict the most appropriate delivery technique, through the induction of data mining models using real data gathered in the perinatal and maternal care unit of Centro Hospitalar of Oporto (CHP). Predicting the type of birth envisions high-quality services, increased safety and effectiveness of specific practices to help guide maternity care decisions and facilitate optimal outcomes in mother and child. In this work was possible to acquire good results, achieving sensitivity and specificity values of 90.11% and 80.05%, respectively, providing the CHP with a model capable of correctly identify caesarean sections and vaginal deliveries