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@article{167395,
        author = {P SRI HARIVAS CHARAN and Dr.G.VENKATA RAMI REDDY},
        title = {ICU Patients Survival rate Prediction with Continuous Deep Learning Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {3},
        pages = {1193-1199},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167395},
        abstract = {The project focuses on the use of deep learning for continuous prediction of mortality in the intensive care unit (ICU). The mortality rate in the ICU is an important metric for assessing hospital clinical quality, and various methods have been proposed for risk stratification of patients. The proposed model in the project aims to overcome the challenge of capturing time sequence information and provide real-time predictions of a patient's risk of death throughout their hospital stay. The model's superior performance allows physicians to pay more attention to high-risk patients and anticipate potential complications, ultimately reducing ICU mortality. The model's performance is evaluated using metrics such as accuracy, F1-score, precision, recall. And also added, ensemble methods, including the Voting Classifier and Stacking Classifier were incorporated in Which voting Classifier achieved remarkable 100% accuracy, To enable user-friendly access and continuous ICU mortality prediction, we’re developing a secure Flask-based front end with streamlined testing and robust authentication.},
        keywords = {deep learning; representation learning; mortality; risk prediction; critical care.},
        month = {August},
        }