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Isabel de la Torre Díez
Researcher at University of Valladolid
Publications - 120
Citations - 2456
Isabel de la Torre Díez is an academic researcher from University of Valladolid. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 17, co-authored 72 publications receiving 1612 citations.
Papers
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Mobile-health
Bruno M. C. Silva,Joel J. P. C. Rodrigues,Isabel de la Torre Díez,Miguel López-Coronado,Kashif Saleem +4 more
TL;DR: A comprehensive review of the state-of-the-art on mobile health services and applications is presented in this paper, where the authors present a deep analysis of the top and novel m-Health services and application proposed by industry.
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Methodological ReviewMobile-health: A review of current state in 2015
Bruno M. C. Silva,Joel J. P. C. Rodrigues,Isabel de la Torre Díez,Miguel López-Coronado,Kashif Saleem +4 more
TL;DR: In this paper, a comprehensive review of the state of the art on mobile health services and applications is presented, and a discussion considering the European Union and United States approaches addressing the m-Health paradigm and directives already published is also considered.
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Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network
TL;DR: The proposed architecture can provide an automated medical diagnostics system to support healthcare specialists for enhanced decision making during this pandemic scenario.
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Social Robots for People with Aging and Dementia: A Systematic Review of Literature.
Susel Góngora Alonso,Sofiane Hamrioui,Isabel de la Torre Díez,Eduardo Motta Cruz,Miguel López-Coronado,Manuel Franco +5 more
TL;DR: From the review of the research articles analyzed, it can be said that use of social robots in elderly people without cognitive impairment and with dementia, help in a positive way to work independently in basic activities and mobility, provide security, and reduce stress.
Journal ArticleDOI
Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods
Mazin Abed Mohammed,Karrar Hameed Abdulkareem,Alaa S. Al-Waisy,Salama A. Mostafa,Shumoos Al-Fahdawi,Ahmed M. Dinar,Wajdi Alhakami,Abdullah Baz,Mohammed Nasser Al-Mhiqani,Hosam Alhakami,Nureize Arbaiy,Mashael S. Maashi,Ammar Awad Mutlag,Begona Garcia-Zapirain,Isabel de la Torre Díez +14 more
TL;DR: The study results revealed that the benchmarking and selection problems associated with COVID19 diagnosis models can be effectively solved using the integration of Entropy and TOPSIS.