Luís Torres Pereira

Abstract
Myocardial infarction, known as heart attack, is one of the leading causes of world death. It occurs when blood heart flow is interrupted by part of coronary artery occlusion, causing the ischemic episode to last longer, creating a change in the patient’s ECG. In this work, a method was developed for predicting patients with MI through Frank 3-lead ECG extracted from Physionet’s PTB ECG Diagnostic Database and using instantaneous frequency and spectral entropy to extract features. Two neural networks were applied: Long Short-Term Memory and Bi-Long Short-Term Memory, obtaining a better result with the first one, with an accuracy of 78%. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
At the University of Applied Sciences Technikum Wien, the intended learning outcomes (ILO) for individual study programs are well defined. These ILO are derived from qualification profiles and should ensure well-educated graduates for professional success. However, at the university level across several study programs, a lack of coordination in ILO development exists. A comparison across study programs and individual courses can show synergies of curricula. It can identify course similarities across programs, allowing collaborative development and standardization with the aim of cost-effective quality improvement. Thus, this paper proposes a solution to this challenge by harmonizing ILO and employing taxonomies for clear outcome classification. Therefore, text analysis, text enrichment with additional information, taxonomy mapping, and the annotation of the intended learning outcomes are the main steps of the prototype.

Abstract
Background: The gap between offer and need for a kidney transplant (KT) has been increasing. The Kidney Donor Profile Index (KDPI) is a measure of organ quality and allows estimation of graft survival, but could not apply to all populations. Knowledge of our kidney donor and recipient population is vital to adjust transplant strategies. Methods: We performed a retrospective evaluation of donors and recipients of KT regarding two kidney transplant units: Centro Hospitalar Universitario de Coimbra, CHUC (Coimbra, Portugal) and Centro Hospitalar Universitario de Sao Joao, CHUSJ (Porto, Portugal), between 2013 and 2018. We then did statistical analysis and modeling, correlating these KT outcomes with donor and recipient characteristics, including KDPI. Artificial intelligence methods were performed to determine the best predictors of graft survival. Results: We analyzed a total of 808 kidney donors and 829 recipients of KT. The association between KDPI and graft dysfunction was only moderate. The decision tree machine learning algorithm proved to be better at predicting graft failure than artificial neural networks. Multinomial logistic regression revealed recipient age as an important prognostic factor for graft loss. Conclusions: In this Portuguese cohort, KDPI was not a good measure of KT survival, although it correlated with GFR 1 year post-transplant. The decision tree proved to be the best algorithm to predict graft failure. Age of the recipient was the most important predictor of graft dysfunction.

Abstract
The concept of health indicates physical, mental and social well-being. Psychological stress is commonly present among freshmen due to social and environmental changes. An approach to study the impact of stress on students relied on biological data assessment. In this work, electrocardiogram signals from first year students, from the Biomedical Engineering course, were collected during an oral presentation, acquiring the RR time series. Linear and nonlinear methodologies are used to extract features that best characterize the RR time series in young subjects under stress conditions.

Abstract
To increase the quality of the health care services and, at the same time, to control their costs, the use of Electronic Health Records (EHR) has substantially increased during the last years. Usability of EHR systems is a key factor to increase their efficiency. The SClinico is an EHR system widely used in public hospitals and primary care centres of the Portuguese National Health Service and the present article reports the assessment of its usability. This usability assessment consisted in three stages: in the first stage, an exploratory assessment was carried out, while in the second stage a quantitative assessment was performed using a validated usability assessment instrument, and, finally, in the third stage a focus group involving clinicians and usability experts was conducted. The results showed that SClinico presents important usability issues and, therefore, recommendations are suggested to overcome the identified issues.