Algoritmo Novedoso Para la Detección de Tareas Repetitivas en el Teclado

González, Bairon Londoño; Sánchez, Pablo Andrés Gutiérrez

doi:10.17081/invinno.3.2.2031

Cited by 7 publications

(1 citation statement)

References 42 publications

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“…This selection largely depends on the knowledge that the data modeler has on the data sources, and it is his/her task to decide whether to include each variable in the model following some previously established criteria. Typically, not all potential variables are equally informative as they may be correlated, present noise, or have no meaningful relationship with the classification (Londoño González & Sánchez, 2015) 3. Classification: Classification involves searching patterns of interest that express dependency on the data and allows groups with similar features to be established.…”

Section: Methodsmentioning

confidence: 99%

Automatic migraine classification using artificial neural networks

2020

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Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients’ health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient’s symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The artificial neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through artificial neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.

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Section: Methodsmentioning

confidence: 99%

Automatic migraine classification using artificial neural networks

2020

View full text Add to dashboard Cite

show abstract

Automatic migraine classification using artificial neural networks

2020

View full text Add to dashboard Cite

Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients’ health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient’s symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.

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Sector BPO: el escenario de los call centers en Barranquilla

Noguera

Palma

Colonna

et al. 2018

Enfoque Latinoamericano

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Durante finales de la primera década e inicios de la segunda década del siglo XXI, la implementación de los centros de llamada, más popularmente conocidos como Call Centers, ha tenido un gran impacto en la ciudad de Barranquilla. A pesar de ser una práctica cuyo origen se remonta a inicios de los años 60 -mundialmente hablando-, y que tuvo un impacto considerable en su momento en el ámbito global, es su auge en este período el que ha tenido mayor impacto dentro del territorio barranquillero, funcionando como una respuesta a aquellas compañías que buscan tomar ventajas para lograr mayor eficiencia en sus labores y tener más énfasis en los servicios tercerizados que realizan las BPOs (Business ProcessOutsourcing) dentro del respectivo Call Center.

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Algoritmo Novedoso Para la Detección de Tareas Repetitivas en el Teclado

Cited by 7 publications

References 42 publications

Automatic migraine classification using artificial neural networks

Automatic migraine classification using artificial neural networks

Automatic migraine classification using artificial neural networks

Sector BPO: el escenario de los call centers en Barranquilla

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