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Chagas Disease

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Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae).

PLoS neglected tropical diseases
Species Distribution Modelling (SDM) determines habitat suitability of a species across geographic areas using macro-climatic variables; however, micro-habitats can buffer or exacerbate the influence of macro-climatic variables, requiring links betwe...

Artificial Inteligence-Based Decision for the Prediction of Cardioembolism in Patients with Chagas Disease and Ischemic Stroke.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
BACKGROUND: Chagas disease (CD) and ischemic stroke (IS) have a close, but poorly understood, association. There is paucity of evidence on the ideal secondary prophylaxis and etiological determination, with few cardioembolic patients being identified...

Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients-The SaMi-Trop cohort.

PLoS neglected tropical diseases
BACKGROUND: Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and reduce mortality. Recently, an artificial intelligence (AI)-enabled ECG algorithm sh...

Two-year death prediction models among patients with Chagas Disease using machine learning-based methods.

PLoS neglected tropical diseases
Chagas disease (CD) is recognized by the World Health Organization as one of the thirteen most neglected tropical diseases. More than 80% of people affected by CD will not have access to diagnosis and continued treatment, which partly supports the hi...

Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease.

PLoS neglected tropical diseases
BACKGROUND: Chagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. I...

Machine learning for predicting Chagas disease infection in rural areas of Brazil.

PLoS neglected tropical diseases
INTRODUCTION: Chagas disease is a severe parasitic illness that is prevalent in Latin America and often goes unaddressed. Early detection and treatment are critical in preventing the progression of the illness and its associated life-threatening comp...

Automated identification of Chagas disease vectors using AlexNet pre-trained convolutional neural networks.

Medical and veterinary entomology
The 158 bug species that make up the subfamily Triatominae are the potential vectors of Trypanosoma cruzi, the etiological agent of Chagas disease. Despite recent progress in developing a picture-based automated system for identification of triatomin...

Weighted-VAE: A deep learning approach for multimodal data generation applied to experimental T. cruzi infection.

PloS one
Chagas disease (CD), caused by the protozoan parasite Trypanosoma cruzi (T. cruzi), represents a major public health concern in most of the American continent and causes 12,000 deaths every year. CD clinically manifests in two phases (acute and chron...

Phytophagous, blood-suckers or predators? Automated identification of Chagas disease vectors and similar bugs using convolutional neural network algorithms.

Acta tropica
Correct identification of blood-sucking bugs, such as triatomines, is important because they are vectors of Chagas' disease. Identifying these insects is often difficult for non-specialists. Deep learning is emerging as a solution for automated ident...

Approaches for handling imbalanced data used in machine learning in the healthcare field: A case study on Chagas disease database prediction.

PloS one
Machine learning has increasingly gained prominence in the healthcare sector due to its ability to address various challenges. However, a significant issue remains unresolved in this field: the handling of imbalanced data. This process is crucial for...