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The document discusses the development of machine learning classifiers, specifically Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for early detection of sepsis using hourly physiological data from ICU patients. The study achieved a prediction accuracy of 90.6% and explored techniques such as data balancing and ensemble methods to enhance performance. The findings suggest that neural networks can effectively predict sepsis up to 12 hours before clinical onset, potentially improvin

Author

vishnuvardhanperugu164

Language

EN