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    Ventilator Pressure Prediction Using a Regularized Regression Model
    (2022)
    Arellano, Amaury
    ;
    Bustamante, Erick
    ;
    Garza, Carlos
    ;
    Ponce, Hiram  
    The mechanical ventilation is one of the most frequent methods used in Intensive Care Units (ICUs) to improve the breathing of patients. During the early days of the COVID-19 pandemic, the use of mechanical ventilators has been crucial. In this work, we propose to build a Lasso regression model based on lung simulators for predicting the airway pressure in the respiratory circuit of ventilators while breathing. We model the whole breathing process in two separate states. After that, we analyze the feature importance in the regression model to better understand the ventilator pressure prediction. We anticipate this model would help improving the patient’s health and overcoming the cost barrier of new methods for mechanical ventilators. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
    Scopus© Citations 2  18  1
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    Statistical Learning Applied to Malware Detection
    (2020)
    Rodríguez Aguilar, Román  
    ;
    Marmolejo Saucedo, José Antonio
    This work shows an application of statistical learning methodologies in order to determine the important factors for malware detection. Support Vector Machines and Lasso Regression performed Malware classification with additional re-sampling methods. The results show that the Lasso Regression allows an efficient selection of relevant variables for the construction of the classifier, also the integration of support vector machines improves the efficiency of the classifier through the application of resampling methods. The model presented in this paper uses a statistical learning approach through the selection of variables, non-linear classification, and resampling methods. © 2020, Springer Nature Switzerland AG.
      16  1