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    Correction: Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3

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    s12911-024-02655-4 (1).pdf (2.890Mb)
    Date
    2024-09-27
    Author
    Rahman, Md Sohanur
    Islam, Khandaker Reajul
    Prithula, Johayra
    Kumar, Jaya
    Mahmud, Mufti
    Alam, Mohammed Fasihul
    Reaz, Mamun Bin Ibne
    Alqahtani, Abdulrahman
    Chowdhury, Muhammad E.H.
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    Abstract
    Background Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques offer advantages over traditional methods for predicting outcomes. This study aimed to develop a prognostic model using a Stacking-based Meta-Classifier to predict 30-day mortality risks in Sepsis-3 patients from the MIMIC-III database.
    URI
    https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85205335920&origin=inward
    DOI/handle
    http://dx.doi.org/10.1186/s12911-024-02685-y
    http://hdl.handle.net/10576/60934
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    • Public Health [‎484‎ items ]

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