A large part of the population does not have access to Emergency Departments or, when they do, face a crowded environment, increasing wait time for the service without their risk situation being assessed. The Manchester Triage System was developed to identify the degree of priority of patients who come to the Emergency Department and to improve the quality of care in emergency services, redefining the flow of care by prioritizing patients who are in the most serious conditions. This work aims to make a comparison between six classifiers, based on the Manchester Triage System, with the data present during patient intake. The purpose is that the model can correctly classify their priority in emergency care. The experiments were conducted with a pediatric emergency database from hospitals in The Netherlands, Portugal and the United Kingdom. With the results obtained by the classifiers' performance, the best performing model was the Random Forest, with 78.20% for accuracy and 78.60% for F1-score. The expectation is that, by automating the classification process, health professionals will have a reliable tool to conduct risk assessment more accurately, having as a side-effect, less crowded Emergency Departments and reducing patient health deterioration due to misclassification and waiting time.

Patents are recognised as an important source of scientific knowledge. The automatic summarisation process of patents can assist in the organisation, and, consequently, the access to the contents of patent databases. The main contribution of this work is to carry out a study of training approaches of a hybrid summarisation model to create concise, single sentence summaries for patent documents. The experiments were executed using a dataset containing more than 80,000 patents, made available by the United States Patent and Trademark Office. Comparative experiments between the selected model and seven state-of-the-art models in extractive, abstractive and hybrid text summarisation (HTS) were performed. The results obtained showed that the selected approach produces better results than extractive and HTS models, and yields good prospects in extremely concise summaries. It is concluded that the study of different training approaches, coupled with the analysis of the attention words weights in the final results, is an important step in this process, impacting directly the choice of the final summarisation model. Besides this, the results of the experiments suggest that the removal of stop words from the input text did not generate better results, although the attention words extracted with the model without stop words were, in general, better.