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.

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