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  • Open Access

    ARTICLE

    Machine Learning Prediction Models of Optimal Time for Aortic Valve Replacement in Asymptomatic Patients

    Salah Alzghoul1,*, Othman Smadi1, Ali Al Bataineh2, Mamon Hatmal3, Ahmad Alamm4

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 455-470, 2023, DOI:10.32604/iasc.2023.038338 - 29 April 2023

    Abstract Currently, the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis (AS) is made by healthcare professionals based on the patient’s clinical biometric records. A delay in surgical aortic valve replacement (SAVR) can potentially affect patients’ quality of life. By using ML algorithms, this study aims to predict the optimal SAVR timing and determine the enhancement in moderate-to-severe AS patient survival following surgery. This study represents a novel approach that has the potential to improve decision-making and, ultimately, improve patient outcomes. We analyze data from 176 patients with moderate-to-severe aortic… More >

  • Open Access

    ARTICLE

    A Novel COVID-19 Prediction Model with Optimal Control Rates

    Ashraf Ahmed1, Yousef AbuHour2,*, Ammar El-Hassan1

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 979-990, 2022, DOI:10.32604/iasc.2022.020726 - 17 November 2021

    Abstract The Corona (COVID-19) epidemic has triggered interest in many fields of technology, medicine, science, and politics. Most of the mathematical research in this area focused on analyzing the dynamics of the spread of the virus. In this article, after a review of some current methodologies, a non-linear system of differential equations is developed to model the spread of COVID-19. In order to consider a wide spectrum of scenarios, we propose a susceptible-exposed-infected-quarantined-recovered (SEIQRS)-model which was analyzed to determine threshold conditions for its stability, and the number of infected cases that is an infected person will… More >

  • Open Access

    ARTICLE

    Modelling the Effect of Self-Immunity and the Impacts of Asymptomatic and Symptomatic Individuals on COVID-19 Outbreak

    M. H. A. Biswas1,*, M. A. Islam1, S. Akter2, S. Mandal2, M. S. Khatun1, S. A. Samad1, A. K. Paul1, M. R. Khatun1

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.3, pp. 1033-1060, 2020, DOI:10.32604/cmes.2020.012792 - 15 December 2020

    Abstract COVID-19 is one of the most highly infectious diseases ever emerged and caused by newly discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has already led the entire world to health and economic crisis. It has invaded the whole universe all most every way. The present study demonstrates with a nine mutually exclusive compartmental model on transmission dynamics of this pandemic disease (COVID-19), with special focus on the transmissibility of symptomatic and asymptomatic infection from susceptible individuals. Herein, the compartmental model has been investigated with mathematical analysis and computer simulations in order to understand… More >

  • Open Access

    ARTICLE

    Application of pediatric Appropriate Use Criteria for initial outpatient evaluation of asymptomatic patients with abnormal electrocardiograms

    Soham Dasgupta1, Shae Anderson1, Michael Kelleman2, Ritu Sachdeva1

    Congenital Heart Disease, Vol.14, No.2, pp. 230-235, 2019, DOI:10.1111/chd.12687

    Abstract Introduction: In the pediatric Appropriate Use Criteria (AUC), abnormal electrocardiogram (ECG) in an asymptomatic patient has been rated as an “Appropriate” indication for transthoracic echocardiogram (TTE). We hypothesized that the yield of abnormal findings on TTE for this indication will be low.
    Methods: All asymptomatic patients (≤ 18 years) from January 1, 2015 to December 31, 2017 who underwent initial outpatient evaluation at our center and had a TTE ordered for an abnormal ECG, were included. Clinic records were reviewed to obtain ECG and TTE findings.
    Results: Of the 199 study patients, 13 (6.5%) had abnormal findings. Incomplete… More >

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