Facultad de Ingeniería

URI permanente para esta comunidad

https://repositorio.uft.cl/handle/20.500.12254/2325

Examinar

Examinando Facultad de Ingeniería por Autor "Candia-Véjar, Alfredo"

Mostrando 1 - 2 de 2
  • Miniatura
    Ítem
    ML models for severity classification and length-of-stay forecasting in emergency units
    (Elsevier, 2023-03-02) Candia-Véjar, Alfredo
    Length-of-stay (LoS) prediction and severity classification for patients in emergency units in a clinic or hospital are crucial problems for public and private health networks. An accurate estimation of these parameters is essential for better planning resources, which are usually scarce. Although it is possible to find several works that propose traditional Machine Learning (ML) models to face these challenges, few works have exploited advances in Natural Language Processing (NLP) on Spanish raw-text vector representations. Consequently, we take advantage of those advances, incorporating sentence embeddings in traditional ML models to improve predictions. Moreover, we apply a strategy based on SHapley Additive exPlanations (SHAP) values to provide explanations for these predictions. The results of our case study demonstrate an increase in the accuracy of the predictions using raw text with a minimum preprocessing. The precision increased by up to 2% in the classification of the patient’s post-care destination and by up to 8% in the prediction of LoS in the hospital. This evidence encourages practitioners to use available text to anticipate the patient’s need for hospitalization more accurately at the earliest stage of the care process.
  • Miniatura
    Ítem
    The Formation of Collaborative Learning Teams in Schools Through Mathematical Optimization for Improving the Classroom Climate
    (IEEE, 2026) Candia-Véjar, Alfredo
    School climate plays a fundamental role in teaching and learning processes, directly influencing the achievement of educational objectives. Moreover, climate-related issues often manifest as bullying, which remains a persistent challenge for education systems worldwide. In this context, teamwork emerges as a key strategy to foster student interaction, strengthen friendship networks, and reduce bullying and aggression. However, traditional team assignment methods—such as student self-selection, teacher allocation, or random distribution—fail to account for the complexity of student interactions and individual characteristics. This study introduces a web-based decision support system designed to assist in team formation within educational settings by utilizing optimization models and algorithms, thereby promoting a more interconnected and inclusive student network. The models incorporate factors such as team diversity, victim protection, and student preferences. Its primary input is a social network along with derived metrics (e.g., betweenness centrality and triadic relationships), which feed into three non-linear integer optimization models that aim to consolidate, create, or enhance student interpersonal relationships. Additionally, the models were evaluated through a large quasi-experiment using both quantitative and qualitative analyses across four dimensions particularly relevant to educational activities: team dynamics, attitude toward the team, team cohesion, and team performance. Empirical results demonstrate that it is possible to improve the classroom climate without compromising the four team-related dimensions. This collaborative learning technology serves as a valuable resource for enhancing collaborative educational strategies and fostering a more inclusive and supportive classroom environment.