AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Brazil

Showing 11 to 20 of 218 articles

Clear Filters

Risk of crashes among self-employed truck drivers: Prevalence evaluation using fatigue data and machine learning prediction models.

Journal of safety research
INTRODUCTION: Transportation companies have increasingly shifted their workforce from permanent to outsourced roles, a trend that has consequences for self-employed truck drivers. This transition leads to extended working hours, resulting in fatigue ...

Machine learning algorithms applied to the diagnosis of COVID-19 based on epidemiological, clinical, and laboratory data.

Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia
OBJECTIVE: To predict COVID-19 in hospitalized patients with SARS in a city in southern Brazil by using machine learning algorithms.

A new approach to dilution prediction of underground mine gold using computing techniques.

Anais da Academia Brasileira de Ciencias
Controlling ore dilution in underground mining is challenging. In this study, data from a Brazilian gold mine were analyzed, covering 70 chambers and 26 variables. Six key variables were identified through decision tree analysis, forming the basis of...

Comparative analysis prediction of prostate and testicular cancer mortality using machine learning: accuracy study.

Sao Paulo medical journal = Revista paulista de medicina
BACKGROUND: The mortality rates of prostate and testicular cancer are higher mortality in the northeast region.

Leveraging artificial neural networks for optimizing Cinnamomum Sintoc essential oil production in Mount Ciremai.

Brazilian journal of biology = Revista brasleira de biologia
Cinnamomum sintoc is a plant renowned for its production of high-quality essential oils. This study assessed the essential oil content in C. sintoc based on its morphological characteristics, environmental conditions, and soil nutrient composition. A...

Sentinel-2 imagery coupled with machine learning to modelling water turbidity in the Doce River Basin, Brazil.

Environmental monitoring and assessment
Remote sensing and machine learning are techniques that can be used to monitor water quality properties, surpassing the limitations of the conventional techniques. Turbidity is an important water quality property directly influenced by the Fundão dam...

Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study.

BMC pregnancy and childbirth
BACKGROUND: Low birth weight (LBW) is a critical factor linked to neonatal morbidity and mortality. Early prediction is essential for timely interventions. This study aimed to develop and evaluate predictive models for LBW using machine learning algo...

Capillariid diversity in archaeological material from the New and the Old World: clustering and artificial intelligence approaches.

Parasites & vectors
BACKGROUND: Capillariid nematode eggs have been reported in archaeological material in both the New and the Old World, mainly in Europe and South America. They have been found in various types of samples, as coprolites, sediments from latrines, pits,...

Forecasting dengue across Brazil with LSTM neural networks and SHAP-driven lagged climate and spatial effects.

BMC public health
BACKGROUND: Dengue fever is a mosquito-borne viral disease that poses significant health risks and socioeconomic challenges in Brazil, necessitating accurate forecasting across its 27 federal states. With the country's diverse climate and geographica...

Machine learning models compared with current clinical indices to predict the outcome of high flow nasal cannula therapy in acute hypoxemic respiratory failure.

Critical care (London, England)
BACKGROUND: Early identification of patients with acute hypoxemic respiratory failure (AHRF) who are at risk of failing high-flow nasal cannula (HFNC) therapy could facilitate closer monitoring, and timely adjustment/escalation of treatment. We aimed...