Geologia, Petrografia e Geoquímica do Batólito Seringa, Província Carajás, SSE do Pará
Resumo: O Granito Seringa, com cerca de 2250 km2 de superfície aflorante, representa o maior batólito da Província Carajás. É intrusivo em unidades arqueanas do Terreno Granito-Greenstone de Rio Maria, sudeste do Cráton Amazônico.
Antonio Lima de Paiva Júnior +2 more
doaj +5 more sources
Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review [PDF]
Background Machine Learning (ML) has been transformative in healthcare, enabling more precise diagnostics, personalised treatment regimens and enhanced patient care.
Joana Seringa +2 more
doaj +2 more sources
Decision-making support systems on extended hospital length of stay: Validation and recalibration of a model for patients with AMI [PDF]
BackgroundCardiovascular diseases are still a significant cause of death and hospitalization. In 2019, circulatory diseases were responsible for 29.9% of deaths in Portugal. These diseases have a significant impact on the hospital length of stay.
Joana Xavier +4 more
doaj +2 more sources
Certificação compulsória e qualidade de agulhas e seringas em um Hospital Sentinela
Seringas e agulhas hipodérmicas são produtos para saúde. Além da tramitação do registro junto à Anvisa, é imprescindível realizar a vigilância pós-comercialização para disponibilizar produtos seguros e eficazes no mercado.
Michele Feitoza-Silva +7 more
doaj +8 more sources
Translation and psychometric validation of the Heart Failure Symptom Tracker (HFaST) [PDF]
Background Heart Failure (HF) is a global public health issue with high morbidity and mortality rates. Symptom management improves HF patients’ quality of life and demonstrates a potential reduction in hospitalisation, particularly among individuals aged
Maria Inês Perez +2 more
doaj +2 more sources
Facilitators, barriers, and recommendations for the implementation of a digital-based case management program: the perspective of Portuguese stakeholders [PDF]
Background Population aging has led to an increase in chronic conditions, highlighting the need for new healthcare models. Digital tools can increase accessibility and promote universal healthcare coverage.
Marta Ferreira +2 more
doaj +2 more sources
Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review protocol [PDF]
Introduction Machine learning (ML) has emerged as a powerful tool for uncovering patterns and generating new information. In cardiology, it has shown promising results in predictive outcomes risk assessment of heart failure (HF) patients, a chronic ...
Joana Seringa +2 more
doaj +2 more sources
Machine learning prediction of mortality in Acute Myocardial Infarction [PDF]
Background Acute Myocardial Infarction (AMI) is the leading cause of death in Portugal and globally. The present investigation created a model based on machine learning for predictive analysis of mortality in patients with AMI upon admission, using ...
Mariana Oliveira +4 more
doaj +2 more sources
Ethical integration of patient-reported outcomes and digital biomarkers in AI healthcare models: an expert consensus framework [PDF]
BackgroundAlongside expected benefits, several ethical concerns arise from Artificial Intelligence (AI) based models. From the design to the implementation and subsequent evaluation, it is crucial to map potential ethical concerns regarding the use of AI
Joana Seringa +3 more
doaj +2 more sources
Use of Artificial Intelligence tools in supporting decision-making in hospital management [PDF]
Background The use of Artificial Intelligence (AI) tools in hospital management holds potential for enhancing decision-making processes. This study investigates the current state of decision-making in hospital management, explores the potential benefits ...
Maurício Alves +3 more
doaj +2 more sources

