Abstract

Abstract

Abstract
Health is one of the most fundamental human rights. In that sense, the creation of health systems attempted to provide the population with organisations, institutions, and resources to meet their needs. However, health inequalities are prevalent in all countries. Thus, evaluating health systems is vital to understand this issue. Accordingly, the aim of this work is to develop a multi-criteria decision analysis (MCDA) approach to innovatively rank nine of the European health systems with Beveridgian financing that would help determine the shortcomings of the Portuguese National Health Service and discern the operating "best practices", in a close interaction with the Portuguese Ministry of Health. First, the panel of decision-making actors was guided through the design of a cognitive map to promote their learning process and help them identify eleven fundamental points of view. Second, their operationalisation was facilitated by selecting acceptable descriptors of performance. Finally, an MCDA approach was proposed to evaluate the chosen health systems using an additive model, which included a sensitivity and a robustness analysis. In the end, the model was perceived by the panel as being trustworthy and reliable. This framework can be used for further MCDA modelling in similar applications based on participatory procedures.

Abstract

Abstract
Education covers a range of sectors from kindergarten to higher education. In the education system, each grade has three possible outcomes: dropout, retention and pass to the next grade. In this work, we study the data from the Department of Statistics of Education and Science (DGEEC) of the Education Ministry. DGEEC maintains those outcomes for each school year, therefore, this study seeks a longitudinal view based on student flow. The document reports the data pre-processing, a stochastic model based on the pre-processed data and a data generation process that uses the previous model. © 2020, Springer Nature Switzerland AG.

Abstract
Evaluation of lung function is central to the management of chronic obstructive respiratory diseases. It is typically evaluated with a spirometer by a specialized health professional, who ensures the correct execution of a forced expiratory manoeuvre (FEM). Audio recording of a FEM using a smart device embedded microphone can be used to self-monitor lung function between clinical visits. The challenge of microphone spirometry is to ensure the validity and reliability of the FEM, in the absence of a health professional. In particular, the absence of a mouthpiece may allow excessive mouth closure, leading to an incorrect manoeuvre. In this work, a strategy to automatically assess the correct execution of the FEM is proposed and validated. Using 498 FEM recordings, both specificity and sensitivity attained were above 90%. This method provides immediate feedback to the user, by grading the manoeuvre in a visual scale, promoting the repetition of the FEM when needed.