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Glucose Data Classification for Diabetic Patient Monitoring

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Glucose Data Classification for Diabetic Patient Monitoring

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Rghioui, A.; Lloret, J.; Parra-Boronat, L.; Sendra, S.; Oumnad, A. (2019). Glucose Data Classification for Diabetic Patient Monitoring. Applied Sciences. 9(20):1-15. https://doi.org/10.3390/app9204459

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Metadatos del ítem

Título: Glucose Data Classification for Diabetic Patient Monitoring
Autor: Rghioui, Amine Lloret, Jaime Parra-Boronat, Lorena Sendra, Sandra Oumnad, Abdelmajid
Entidad UPV: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Fecha difusión:
Resumen:
[EN] Living longer and healthier is the wish of all patients. Therefore, to design effective solutions for this objective, the concept of Big Data in the health field can be integrated. Our work proposes a patient monitoring ...[+]
Palabras clave: Internet of Things , Big Data , Healthcare , Machine learning , Diabetes , Blood glucose
Derechos de uso: Reconocimiento (by)
Fuente:
Applied Sciences. (eissn: 2076-3417 )
DOI: 10.3390/app9204459
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/app9204459
Código del Proyecto:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84802-C2-1-P/ES/RED COGNITIVA DEFINIDA POR SOFTWARE PARA OPTIMIZAR Y SECURIZAR TRAFICO DE INTERNET DE LAS COSAS CON INFORMACION CRITICA/
Agradecimientos:
This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de ...[+]
Tipo: Artículo

References

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Rghioui, A., Sendra, S., Lloret, J., & Oumnad, A. (2016). Internet of Things for Measuring Human Activities in Ambient Assisted Living and e-Health. Network Protocols and Algorithms, 8(3), 15. doi:10.5296/npa.v8i3.10146

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