ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus
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Percorrer ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus por Objetivos de Desenvolvimento Sustentável (ODS) "03:Saúde de Qualidade"
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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.
- Coagulation treatment for olive oil pomace extraction wastewaterPublication . Grabowski, Thais; Martins, RamiroThe European industry is well-established in the production of olive oil. The valorization of olive pomace by extraction generates pollutant effluent because of waste leaching and processing in these extractor units (OOEIW). The goal of this study was to use the coagulation process to treat OOEIW. The effects of five different coagulants, pH, and flocculant addition were also investigated. Al-2(SO4)(3), FeCl3 and MgCl2 achieved maximum removal of 98% total phenolic compounds (TPh) and 46% chemical oxygen demand (COD). pH had a significant effect on TPh removal with Al-2(SO4)(3). Flocculant addition had little impact on pollutant removal but enhanced sludge appearance. The optimal coagulation conditions were 9 g L-1 Al-2(SO4)(3), pH 7, and 50 mg L-1 Rifloc F45, resulting in the removal of 40% of COD and 80% of TPh.
- Colorectal Polyp Segmentation: Impact of Combining Different Datasets on Deep Learning Model PerformancePublication . Araujo, Sandro Luis de; Scheeren, Michel Hanzen; Aguiar, Rubens Miguel Gomes; Mendes, Eduardo; Franco, Ricardo Augusto Pereira; Paula Filho, Pedro Luiz deColorectal cancer is a major health concern, ranking as one of the most common and deadly forms of cancer. It typically begins as polyps, which are abnormal growths in the intestinal mucosa. Identifying and removing these polyps through colonoscopy is a crucial preventative measure. However, even experienced professionals can overlook some polyps during examinations. In this context, segmentation algorithms can assist medical professionals by identifying areas in an image that correspond to a polyp. These algorithms, which rely on deep learning, require extensive image datasets to effectively learn how to identify and segment polyps. This study aimed to identify public colonoscopy image datasets that contain polyps and to examine how combining these datasets might affect the performance of a deep learning-based segmentation algorithm. After selecting the datasets and defining their combinations, we trained a segmentation algorithm on each combination. The evaluation of the trained models showed that merging datasets can enhance model generalization, with increases of up to 0.242 in the dice coefficient and 0.256 in the Intersection over Union (IoU). These improvements could lead to higher diagnostic accuracy in clinical settings, enhancing efforts to prevent colorectal cancer.
- 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.
- EEG monitoring in driving using embedded systemsPublication . Alves, Rui; Matos, PauloEpilepsy is a disease that can appear in all age groups, where many patients only have their diagnoses confirmed at more advanced ages and at different stages of their life. This disease manifests through partial and/or total seizures. To avoid more drastic consequences, an accurate diagnosis of seizures is essential to provide the correct medication to the patient. Although many patients are able to control seizures using a single drug, others need multiple medications or even complementary measures. In addition, epilepsy can make the quality of life substantially difficult due to seizures, however, it can also reduce the autonomy of patients in day-to-day tasks such as driving - in most countries, after the diagnosis of epilepsy, the patient is inhibited from driving for a period of time. This paper, through a set of chips and sensors, provides a solution to identify seizures through the study of a patient’s electroencephalogram (EEG) waves, then applies several safety measures to protect the patient while driving.
- Event-driven management system for smart pill dispensersPublication . Van-Deste, Isaac; Costa, Bruno A.; Lopes, Rui Pedro; Pereira, Ana I.Non-adherence on medication is increasing and there are several problems associated with it. There are many challenges in order to have full adherence to the prescribed treatments. Smart pill dispensers are being a new way to support patients who have complex prescriptions. However, those dispensers do not have much capacity and are very simple machines with few components. This paper presents a new architecture for smart pills dispensers based on Events-Driven, a distributed architecture where all the components have weak associations with each other. This architecture proves to be very advantageous for smart pill dispensers, increasing the flexibility and the resilience of these machines.
- Evolution of Demand for Portuguese Thermal Spas: An Exploratory Data Analysis of Administrative DataPublication . Alves, Jéssica; Alves, Maria José; Nunes, AlcinaWell-being or positive health is not a new concept; However, since the mid-twentieth century, scientific institutions have addressed the issue and its implications for medicine and public health. It can be observed that wellness and healthy living are becoming more and more important in society. Consequently, there is na increased need to look for products and services that mitigate the risk of these diseases associated with modern society. Thus, a fusion between tourism and wellness emerges, with thermal spas being an essential factor in the health and wellness promotion equation. In Portugal, thermal services are well known. However, the demand for its services is subject to difficulties well observed during the COVID-19 pandemic. Therefore, it is fundamental to characterize the Portuguese thermal market on the demand side to segment it and find the best strategy to meet the user needs. Secondly, it is relevant to understand the weight of each segment in the market. Focusing on the division between classic thermal services—considered therapeutical health services—and wellness services provided by Portuguese termal establishments, this research aims to characterize the thermal market on the demand side. In particular, the study seeks to understand how wellness services are evolving regarding the total demand for Portuguese thermal establishments. Based on the descriptive analysis of administrative data of registers in all the Portuguese termal establishments, it is possible to conclude that the increasing demand for thermalism products and services (both therapeutic and wellness) suffered a severe crash with the financial crisis and the Covid-19 pandemic. Age is an essential factor that distinguishes the market segment of the therapeutic thermal offer. The consumers of such a thermal component are older than the ones demanding wellness services. Finally, specific international markets (France, Spain, and the United Kingdom) become more important in the user’s segments.
- 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.
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