Predicting Hospital Mortality For ICU Patients Using Time Series Analysis DID: 1226 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 CiteDr. 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. |