ISSN No:2250-3676
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Scholarly Peer Reviewed and Fully Referred Open Access Multidisciplinary Monthly Research Journal


    Predicting Hospital Mortality For ICU Patients Using Time Series Analysis D

    Dr. M.V.Vijaya Saradhi, G.Sathvika, M.Pranathi, J.Jeevan, A.Yuvaraj

    Author

    ID: 1226

    DOI: HTTPS://DOI.ORG/10.5281/zenodo.15624241

    Abstract :

    ICU Patient Sanitarium Mortality Vaticination Is A Crucial Area For Enhancing Patient Issues And Resource Operation. In This Study, We Introduce A Machine Literacygrounded Time Series Analysis Channel That Utilizes Longitudinal Patient Information To Prognosticate Threat Of Mortality By Employing Models Like LSTM, GRU, And Transformer Networks. The MIMIC- IV Dataset Was Employed For Our Trials, And The Original 48 Hours Of ICU Admission Were Used To Make Largely Accurate Prophetic Models. Our Motor Model Realized An AUROC Of 0.92 And Outperformed Being Traditional Nascences By A Wide Periphery. Keywords — ICU Mortality, Time Series, MIMIC- IV, LSTM, Transformer, Clinical Decision Support, Deep Learning

    Published:

    10-6-2025

    Issue:

    Vol. 25 No. 6 (2025)


    Page Nos:

    442-446


    Section:

    Articles

    License:

    This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

    How to Cite

    Dr. M.V.Vijaya Saradhi, G.Sathvika, M.Pranathi, J.Jeevan, A.Yuvaraj, Predicting Hospital Mortality for ICU Patients Using Time Series Analysis D , 2025, International Journal of Engineering Sciences and Advanced Technology, 25(6), Page 442-446, ISSN No: 2250-3676.

    DOI: HTTPS://DOI.ORG/10.5281/zenodo.15624241