AIMC Topic: Brazil
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Analysis of the Predictors of Mortality from Ischemic Heart Diseases in the Southern Region of Brazil: A Geographic Machine-Learning-Based Study.
Global heart
Nov 27, 2024
BACKGROUND: Mortality due to ischemic heart disease (IHD) is heterogeneously distributed globally, and identifying the sites most affected by it is essential in developing strategies to mitigate the impact of the disease, despite the complexity resul...
Risk of crashes among self-employed truck drivers: Prevalence evaluation using fatigue data and machine learning prediction models.
Journal of safety research
Nov 16, 2024
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 ...
Predictive modeling of gestational weight gain: a machine learning multiclass classification study.
BMC pregnancy and childbirth
Nov 8, 2024
BACKGROUND: Gestational weight gain (GWG) is a critical factor influencing maternal and fetal health. Excessive or insufficient GWG can lead to various complications, including gestational diabetes, hypertension, cesarean delivery, low birth weight, ...
Academic-related stressors predict depressive symptoms in graduate students: A machine learning study.
Behavioural brain research
Nov 7, 2024
BACKGROUND: Graduate students face higher depression rates worldwide, which were further exacerbated during the COVID-19 pandemic. This study employed a machine learning approach to predict depressive symptoms using academic-related stressors.
Development and Evaluation of a Convolutional Neural Network for Microscopic Diagnosis Between Pleomorphic Adenoma and Carcinoma Ex-Pleomorphic Adenoma.
Head & neck
Oct 27, 2024
AIMS: To develop a model capable of distinguishing carcinoma ex-pleomorphic adenoma from pleomorphic adenoma using a convolutional neural network architecture.
Machine learning-based risk prediction for major adverse cardiovascular events in a Brazilian hospital: Development, external validation, and interpretability.
PloS one
Oct 11, 2024
BACKGROUND: Studies of cardiovascular disease risk prediction by machine learning algorithms often do not assess their ability to generalize to other populations and few of them include an analysis of the interpretability of individual predictions. T...
Plastic debris detection along coastal waters using Sentinel-2 satellite data and machine learning techniques.
Marine pollution bulletin
Oct 10, 2024
Few studies have effectively shown how to use satellites that gather optical data to monitor plastic debris in the marine environment. For the first time, floating macro-plastics distinguishable from seaweed are identified in optical data from the Eu...
Development of HepatIA: A computed tomography annotation platform and database for artificial intelligence training in hepatocellular carcinoma detection at a Brazilian tertiary teaching hospital.
Clinics (Sao Paulo, Brazil)
Oct 9, 2024
BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent tumor with high mortality rates. Computed tomography (CT) is crucial in the non-invasive diagnosis of HCC. Recent advancements in artificial intelligence (AI) have shown significant potential ...
The fuzzy system ensembles entomological, epidemiological, demographic and environmental data to unravel the dengue transmission risk in an endemic city.
BMC public health
Sep 27, 2024
BACKGROUND: The effectiveness of dengue control interventions depends on an effective integrated surveillance system that involves analysis of multiple variables associated with the natural history and transmission dynamics of this arbovirus. Entomol...
Machine Learning Approach to Support Taxonomic Discrimination of Mayflies Species Based on Morphologic Data.
Neotropical entomology
Sep 25, 2024
Artificial intelligence (AI) and machine learning (ML) offer objective solutions in the elaboration of taxonomic keys, such as the processing of large numbers of samples, aiding in the species identification, and optimizing the time required for this...
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