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Minería de datos aplicados a datos biológicos

Authors: Vidal Miralles, José Armando;

Minería de datos aplicados a datos biológicos

Abstract

En diciembre de 2019 se detectó una nueva enfermedad respiratoria aguda, la enfermedad denominada como COVID-19 (coronavirus disease) en la ciudad de Wuhan, provincia de Hubei, China. Desde el momento en el que se decretó la enfermedad como pandémica en marzo de 2019, los gobiernos con competencias en la gestión de la pandemia han impuesto diferentes medidas para mitigar la propagación del virus SARS-CoV-2. Esta situación demanda la necesidad de conocer con la máxima precisión posible la evolución de la enfermedad en cada región para así tomar las mejores decisiones en la gestión de la pandemia. Una de las metodologías más usadas durante la pandemia para predecir la propagación de la enfermedad fue la construcción de redes neuronales artificiales (ANN). Con este método, basado en el aprendizaje profundo, se ha realizado una predicción de la COVID-19 de diferentes municipios de la Comunidad de Madrid. Las predicciones a partir de redes neuronales podrían cambiar drásticamente la gestión de esta y futuras pandemias, dejando entrever la posibilidad de que las medidas sean más específicas para cada región.

Country
Spain
Related Organizations
Keywords

Madrid, Estadística e Investigación Operativa, COVID-19, Contagios, Regresión lineal, Red neuronal, Incidencia

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
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