ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus
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Percorrer ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus por Domínios Científicos e Tecnológicos (FOS) "Ciências Médicas::Ciências da Saúde"
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- Classical Versus Wellness Thermalism: The Case of Portuguese Thermal Establishments Before and After the COVID-19 PandemicPublication . Alves, Maria José; Nunes, Alcina; Alves, Jéssica; Gonçalves, Estelle SilvaThermal/mineral springs are one of the fastest-growing subcategories of wellness tourism. Indeed, it is an activity that has steadily increased in all of Europe’s developed economies over the last few decades. The pandemic has raised awareness of the importance of healthy lifestyles and has subsequently led to a surge in consumption of experiences and travel, somehow motivated by wellness. This study analyses the evolution of thermal users’ alternation between wellness and classical thermalism in Portugal. The objective is achieved by applying exploratory and cluster data analysis to a Portuguese administrative database containing the number of user registers and revenues generated from 2012 to 2022. During this period, the wellness registers increased in most thermal establishments compared to the classic records, even if service diversification may be found in most thermal establishments. Still, the financial value added by wellness consumers does not seem to follow the previously observed shift. The establishments with more classical registers are still the ones that are able to generate the highest income per person.
- Comparison of neural network architectures for diabetes predictionPublication . Guerreiro, Nathan Antonio; Nijo, Rui; Teixeira, João PauloDiabetes represents a significant global health challenge, with millions of individuals affected and substantial impacts on healthcare systems. In this study, we compare two neural network architectures for diabetes prediction: the Feedforward Neural Network (FFNN) and the Cascade-Forward Backpropagation Neural Network (CFBPNN). Utilizing the Diabetes Prediction Dataset, comprising 100,000 samples, and after a balanced result, 17,000 samples were obtained. The networks are trained using the Levenberg-Marquardt and Resilient Backpropagation algorithms, and performance metrics, including precision, sensitivity, specificity, accuracy, F1-score, and computational time, are evaluated. Results indicate that the FFNN architecture paired with the Levenberg-Marquardt algorithm demonstrates superior diagnostic prediction accuracy with 91,10%. However, this comes at the cost of longer computational time compared to the CFBPNN.
- D.R.E.A.M. App to Promote the Mental Health in Higher Education StudentsPublication . Vaz, Clara B.; Galvão, Ana Maria; Pais, Clarisse; Pinheiro, MarcoThis paper presents the development process of the mobile App D.R.E.A.M., Design-thinking to Reach-out, Embrace and Acknowledge Mental health, which is a tool for self-assessment and self-care in promoting the mental health of higher education students. In Portugal, the program for promoting Mental Health in higher education advocates the development and use of digital tools, such as apps and/or social networks and platforms, aimed at promoting wellbeing and with the potential for use to be more accessible to higher education students. The objective of this app is to promote the mental health and wellbeing of higher education students. Design Thinking was used as the methodology for building the app, which was developed using a combination of low-code/no-code tools, Flutter/Dart coding, and Google’s Firebase capabilities and database functionalities. In the first semester of the 2023/2024 academic year, 484 students downloaded the app, and 22 emails were received for psychological consultations. A dynamic update of the app is required, with modules on time management and study organization, structured physical activity programs, development of socio-ntrepreneurial skills, and vocational area.
- Digital innovation in health care: addressing medication non-adherencePublication . Bhandari, Laxmi; Fonseca, Manuel José; Fernandes, António B. ; Garcia, Jorge EsparteiroNon-adherence to medication is a pervasive issue worldwide, affecting 50% of prescription users, resulting in suboptimal therapy outcomes and premature death. One of the key factors contributing to non-adherence is the complexity associated with managing medication regimens. To address this challenge, an automated pill dispenser “SelfMed, your medication partner” has been proposed. This study focuses on studying determinants of medication non-adherence, its ramifications and alternatives available in the market in order to increase medication adherence among adults aged 60 and over. The overarching goal of this research is to evaluate whether digital solutions like SelfMed are required for addressing non-compliance issues in Portugal while assessing their effectiveness over time for our target audience within the marketplace. The research was conducted using primary data collected through a questionnaire distributed to users and care institutions/companies in Portugal (Northern area). According to the analysis, 40% of users and 40% of care companies are interested in obtaining SelfMed to simplify the complex medication management and prescription regimen for the end user.
- Forecasting COVID-19 in european countries using long short-term memoryPublication . Carvalho, Kathleen; Teixeira, Rita de Almeida; Reis, Luis Paulo; Teixeira, João PauloEffective time series forecasts are increasingly important in supporting judgment in various decisions. Various prediction models are available to support these projections based on how each area provides a diverse set of data with variable behavior. Artificial neural networks (ANNs) significantly contribute to medical research since using predictive ideas allows for the study of disease progression in the future, as well as the behavior of other variables. This study implemented the proposed model based on Long Short-Term Memory (LSTM) to forecast COVID-19 daily new cases, deaths, and ICU patients. The methodology uses quantitative and qualitative data from six European countries: Austria, France, Germany, Italy, Portugal, and Spain to predict the last 242 days of the COVID-19 pandemic. The dataset uses the healthcare parameters of the number of daily new cases, deaths, ICU patients, and mitigation procedures, such as the percentage of the population fully vaccinated, the mandatory use of masks, and the lockdown. Two approaches were used to evaluate the model’s performance: the mean absolute error (MAE) and the mean square error (MSE). The results demonstrate that the LSTM model efficiently captures general trends in COVID-19 metrics but shows limitations when predicting data with low values or high variability, such as daily deaths. The model reported the lowest errors for Spain and Portugal, while France and Germany exhibited higher error rates due to differences in data reporting and pandemic dynamics. These findings highlight the importance of contextualizing predictive models based on specific regional characteristics.
- Influence of habits and comorbidities on liver diseasePublication . Leite, Gabriel A.; Azevedo, Beatriz Flamia; Pacheco, Maria F.; Fernandes, Florbela P.; Pereira, Ana I.The prevalence of hepatocellular carcinoma is expected to continue increasing worldwide, and its difficulty in early detection highlights the need for advanced monitoring technologies. As the disease progresses, it has a serious impact on patients’ health, and in severe cases, liver transplantation becomes the only viable solution, reinforcing its importance as a global health problem. This study proposes the use of different artificial intelligence methods to compare and understand them related to liver disease. Well-known algorithms such as Random Forest and Multi-Layer Perceptron were tested, as well as ensemble methods that exploit different modeling structures. The results showed that AdaBoost, Random Forest, and Gradient Boosting performed best with Area Under the Curve of 0.89, 0.86, and 0.84 respectively. To analyze their influence on clinical results, the best-performing model was reapplied only to the non-biochemical features that compose the dataset. The results indicate that portal vein thrombosis, diabetes, and hypertension are the most influential variables, with contributions of 29.48%, 20.50%, and 16.60%, respectively.
- Interconnection between lifestyle, health, and academic outcomes: an analysis on study habits and well-beingPublication . Azevedo, Beatriz Flamia; Bezerra, Ana J.B.; Sirmakessis, Spiros; Pereira, Ana I.Balancing academic demands with personal and professional responsibilities has become an increasingly challenging task, making it difficult to maintain well-being and potentially leading to serious health problems. The stress resulting from these multiple daily tasks, combined with the pressure to perform at high academic levels, directly impacts students’ mental and emotional health, significantly compromising their quality of life. In this work, statistical and clustering techniques are employed to analyze the dataset “Daily Lifestyle and Academic Performance of Students”. The objective of this work is to explore the relationship between students’ daily habits, level of stress, and the impact on academic performance. The results point out that many students have difficulty managing time and maintaining well-being (low-stress levels) with high academic performance since, according to the results, the higher the academic outcome, the higher the student’s stress level.
- An openmodelica package for BELBICPublication . Coelho, João Paulo; Coelho, J. A. B.; Braz-César, ManuelThis paper presents the development and implementation of a new package for OpenModelica that integrates the Brain Emotional Learning Based Intelligent Controller (BELBIC) approach into control system simulations. BELBIC, inspired by neurobiological models of emotional learning, has demonstrated effectiveness in handling complex, nonlinear, and adaptive control problems. The proposed package provides a modular and user-friendly framework for integrating BELBIC enabling researchers and engineers to design, simulate, and analyze intelligent control strategies within an open-source environment. Key features of the package include customizable emotional response parameters and compatibility with existing Modelica libraries. To validate the package, a set of examples are included which demonstrates its application to the control of common dynamic systems.
- Recreate immersive scenarios for practicing sports in gyms - user applicationPublication . Pereira, Hélder; Matos, PauloThis paper presents the development of a mobile application to integrate immersive features into fitness devices to enhance user experience and athletic performance. The proposed architecture ensures compatibility with a wide range of fitness equipment, regardless of brand or model. Central to the system is the playback of authentic route videos, augmented by contextual data such as speed, altitude, and environmental conditions. This allows dynamic equipment control, adjusting parameters like resistance and slope for a more consistent immersive experience. The application acts as a central interface, managing video recordings and data collection, all synchronized through a cloud-based platform that builds a community-driven repository of shared routes. Additionally, the application integrates cloud and edge computing alongside artificial intelligence to provide tailored workout adjustments based on historical performance and real-time metrics. Communication between the application, fitness devices, and wearables relies on SIG BLE standards. Users can create new immersive routes and download shared ones for enhanced training sessions. The paper elaborates on the application's architectural design and technological choices while discussing implementation progress, challenges, and future improvements.
- Schizophrenia diagnosis support with spectral and cepstral features of speechPublication . Teixeira, Felipe; Mendes, João; Soares, Salviano F.P.; Abreu, J.L. Pio; Teixeira, João PauloSchizophrenia is a severe mental illness affecting over 20 million people worldwide, significantly impairing quality of life and daily functioning. Current diagnostic methods rely heavily on subjective assessments and interactions between doctors and patients, leaving room for potential misdiagnoses. Recent advancements in technology have introduced non-invasive, fast, and user-friendly approaches, such as machine learning, to support psychiatric diagnosis. In this study, spectral features extracted from speech samples of individuals with and without schizophrenia were analyzed. Using an ensemble bagged tree model, we achieved an accuracy of 96.3%, a sensitivity of 94.6%, and an F1-score of 95.4%. These results highlight the potential of speech-based machine learning models as effective tools for aiding schizophrenia diagnosis.