Examinando por Autor "Salinas, Matías"

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    A Step Forward to Formalize Tailored to Problem Specificity Mathematical Transforms
    (Frontiers Media S.A., 2022-06) Glaría, Antonio; Salas, Rodrigo; Chabert, Stéren; Roncagliolo, Pablo; Arriola, Alexis; Tapia, Gonzalo; Salinas, Matías; Zepeda, Herman; Taramasco, Carla; Oshinubi, Kayode; Demongeot, Jacques
    Linear functional analysis historically founded by Fourier and Legendre played a significant role to provide a unified vision of mathematical transformations between vector spaces. The possibility of extending this approach is explored when basis of vector spaces is built Tailored to the Problem Specificity (TPS) and not from the convenience or effectiveness of mathematical calculations. Standardized mathematical transformations, such as Fourier or polynomial transforms, could be extended toward TPS methods, on a basis, which properly encodes specific knowledge about a problem. Transition between methods is illustrated by comparing what happens in conventional Fourier transform with what happened during the development of Jewett Transform, reported in previous articles. The proper use of computational intelligence tools to perform Jewett Transform allowed complexity algorithm optimization, which encourages the search for a general TPS methodology. Copyright © 2022 Glaría, Salas, Chabert, Roncagliolo, Arriola, Tapia, Salinas, Zepeda, Taramasco, Oshinubi and Demongeot.
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    Ítem
    Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023-02) Torres, Romina; Zurita, Christopher; Mellado, Diego; Nicolis, Orietta; Saavedra, Carolina; Tuesta, Marcelo; Salinas, Matías; Bertini, Ayleen; Pedemonte, Oneglio; Querales, Marvin; Salas, Rodrigo
    Cardiovascular diseases represent the leading cause of death worldwide. Thus, cardiovascular rehabilitation programs are crucial to mitigate the deaths caused by this condition each year, mainly in patients with coronary artery disease. COVID-19 was not only a challenge in this area but also an opportunity to open remote or hybrid versions of these programs, potentially reducing the number of patients who leave rehabilitation programs due to geographical/time barriers. This paper presents a method for building a cardiovascular rehabilitation prediction model using retrospective and prospective data with different features using stacked machine learning, transfer feature learning, and the joint distribution adaptation tool to address this problem. We illustrate the method over a Chilean rehabilitation center, where the prediction performance results obtained for 10-fold cross-validation achieved error levels with an NMSE of (Formula presented.) and an (Formula presented.) of (Formula presented.), where the best-achieved performance was an error level with a normalized mean squared error of 0.008 and an (Formula presented.) up to (Formula presented.). The results are encouraging for remote cardiovascular rehabilitation programs because these models could support the prioritization of remote patients needing more help to succeed in the current rehabilitation phase. © 2023 by the authors.