ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus
URI permanente para esta coleção:
Navegar
Percorrer ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus por Domínios Científicos e Tecnológicos (FOS) "Ciências Médicas::Biotecnologia Médica"
A mostrar 1 - 2 de 2
Resultados por página
Opções de ordenação
- 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.
- 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.