Browsing by Author "Marques, Catarina"
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- The ECSI model in higher education in tourism: A segmentation analysis in the Portuguese casePublication . Eurico, Sofia; Pinto, Patrícia; Silva, João Albino; Marques, CatarinaThis research explores the European Consumer Satisfaction Index model applied to higher education in tourism by including the construct of employability and by accounting for unobserved heterogeneity. In particular, it intends to identify segments of higher education institutions' (HEI) consumers based on the structural model estimates of the European Consumer Satisfaction Index (ECSI), enlarged with the employability construct. A model-based segmentation approach using FIMIX in PLS path modelling is used. The ECSI is properly adjusted to the educational framework and shows its effectiveness when assessing students' satisfaction regarding the attended HEI. Two distinctive graduate segments are identified using a sample of 166 HEI consumers. The results confirm the assumption of heterogeneity as the relationships differ across segments and the need for HEIs to target those segments differently in such a competitive context. © 2018 Institute for Tourism. All rights reserved.
- STM-GNN: Space-Time-and-Memory Graph Neural Networks for Predicting Multi-Drug Resistance Risks in Dynamic Patient NetworksPublication . Geissbuhler, Damien; Bornet, Alban; Marques, Catarina; Anjos, André; Pereira, Sónia; Teodoro, DouglasHospital-acquired infections (HAIs), particularly those caused by multidrug-resistant (MDR) bacteria, pose significant risks to vulnerable patients. Accurate predictive models are important for assessing infection dynamics and informing infection prediction and control (IPC) programmes. Graph-based methods, including graph neural networks (GNNs), offer a powerful approach to model complex relationships between patients and environments but often struggle with data sparsity, irregularity, and heterogeneity. We propose the space-time-and-memory (STM)-GNN, a temporal GNN enhanced with recurrent connectivity designed to capture spatiotemporal infection dynamics. STM-GNN effectively integrates sparse, heterogeneous data combining network information from patient-environment interactions and internal memory from historical colonization and contact patterns. Using a unique IPC dataset containing clinical and environmental colonization information collected from a long-term healthcare unit, we show that STM-GNN effectively addresses the challenges of limited and irregular data in an MDR prediction task. Our model reaches 0.84 AUROC, and achieves the most balanced performance overall compared to classic machine learning algorithms, as well as temporal GNN approaches.