AIMC Topic: Brazil

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Trace elements and machine learning for Brazilian beef traceability.

Food chemistry Jul 4, 2020
Brazilian livestock with a herd of more than 215 million animals is distributed over a vast area of 160 million hectares, leading the country to the first position in the world beef exports and second in beef production and consumption. Animals risen...
Ecosystem Brazil Trace Elements Neural Networks, Computer Cattle Red Meat Machine Learning Animals
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Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population.

International immunopharmacology Jun 16, 2020
Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic COVID-19. Early symptoms overlap with other common conditions such as common cold and Influenza, making early screening and diagnosis are crucial go...
Clinical Laboratory Techniques Betacoronavirus Humans Pneumonia, Viral Neural Networks, Computer Machine Learning Pandemics Prognosis Models, Statistical COVID-19 SARS-CoV-2 Coronavirus Infections Brazil COVID-19 Testing ROC Curve Datasets as Topic Mass Screening Blood Cell Count
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Prediction of depression cases, incidence, and chronicity in a large occupational cohort using machine learning techniques: an analysis of the ELSA-Brasil study.

Psychological medicine Jun 4, 2020
UNLABELLED: .
Machine Learning Adult Brazil Depression Incidence Humans Cohort Studies Longitudinal Studies
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Detection and identification of Cannabis sativa L. using near infrared hyperspectral imaging and machine learning methods. A feasibility study.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Apr 21, 2020
Remote identification of illegal plantations of Cannabis sativa Linnaeus is an important task for the Brazilian Federal Police. The current analytical methodology is expensive and strongly dependent on the expertise of the forensic investigator. A fa...
Sensitivity and Specificity Brazil Spectroscopy, Near-Infrared Plant Leaves Principal Component Analysis Machine Learning Reproducibility of Results Feasibility Studies Cannabis Hyperspectral Imaging Cheminformatics
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An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection.

Scientific reports Jan 8, 2020
Bees play a key role in pollination of crops and in diverse ecosystems. There have been multiple reports in recent years illustrating bee population declines worldwide. The search for more accurate forecast models can aid both in the understanding of...
Ecosystem Brazil Bees Crops, Agricultural Pollination Neural Networks, Computer Animals Feeding Behavior Behavior, Animal Forestry
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Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer.

International journal of medical informatics Dec 28, 2019
BACKGROUND: The proper estimate of the risk of recurrences in early-stage oral tongue squamous cell carcinoma (OTSCC) is mandatory for individual treatment-decision making. However, this remains a challenge even for experienced multidisciplinary cent...
Chemoradiotherapy Finland Mouth Neoplasms Aged Neoplasm Recurrence, Local Child Aged, 80 and over Combined Modality Therapy Support Vector Machine Young Adult Supervised Machine Learning Decision Trees Adult Tongue Neoplasms Bayes Theorem Male Humans Middle Aged Algorithms Brazil Prognosis Adolescent Female Incidence Cohort Studies
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Potential Confounders in the Analysis of Brazilian Adolescent's Health: A Combination of Machine Learning and Graph Theory.

International journal of environmental research and public health Dec 21, 2019
The prevalence of health problems during childhood and adolescence is high in developing countries such as Brazil. Social inequality, violence, and malnutrition have strong impact on youth health. To better understand these issues we propose to combi...
Adolescent Health Social Class Humans Machine Learning Female Male Nutrition Assessment Brazil Adolescent Socioeconomic Factors
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Machine learning to predict 30-day quality-adjusted survival in critically ill patients with cancer.

Journal of critical care Nov 2, 2019
PURPOSE: To develop and compare the predictive performance of machine-learning algorithms to estimate the risk of quality-adjusted life year (QALY) lower than or equal to 30 days (30-day QALY).
Adult Sensitivity and Specificity Male Middle Aged Brazil Neoplasms Quality of Life ROC Curve Decision Trees Area Under Curve Critical Illness Humans Intensive Care Units Hospitals, Public Female Machine Learning Hospitalization Signal Processing, Computer-Assisted Algorithms Aged Probability Prospective Studies Pattern Recognition, Automated Prognosis False Positive Reactions
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Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction.

JACC. Heart failure Oct 9, 2019
OBJECTIVES: This study sought to develop models for predicting mortality and heart failure (HF) hospitalization for outpatients with HF with preserved ejection fraction (HFpEF) in the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with...
Argentina Stroke Volume Middle Aged Risk Assessment Brazil Canada Humans Risk Factors ROC Curve Machine Learning United States Hospitalization Prognosis Health Status Aged Female Double-Blind Method Survival Rate Heart Failure Male
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ICU staffing feature phenotypes and their relationship with patients' outcomes: an unsupervised machine learning analysis.

Intensive care medicine Oct 8, 2019
PURPOSE: To study whether ICU staffing features are associated with improved hospital mortality, ICU length of stay (LOS) and duration of mechanical ventilation (MV) using cluster analysis directed by machine learning.
Intensive Care Units Brazil Organ Dysfunction Scores Length of Stay Personnel Staffing and Scheduling Hospital Mortality Nurses Humans Physical Therapists Time Factors Logistic Models Physicians Hospital Bed Capacity Cluster Analysis Retrospective Studies Unsupervised Machine Learning Odds Ratio
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