Abstract

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.