Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Lizzy Angelin Alphonsa, G.; Mary Jasmine, E.; Rajavel, Rajkumar Title A name given to the resource Early Sepsis Prediction using Hybrid LightGBM and LSTM Model Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Conference on NexGen Networks and Cybernetics, IC2NC 2025 - Proceedings;pp.794-800 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC2NC67409.2025.11376300" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC2NC67409.2025.11376300</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035598002?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035598002?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Lizzy Angelin Alphonsa G., Christ University, Department of AI, Ml &amp; Data Science, Bangalore, India; Mary Jasmine E., Christ University, Department of AI, Ml &amp; Data Science, Bangalore, India; Rajavel R., Christ University, Department of AI, Ml &amp; Data Science, Bangalore, India Description An account of the resource Sepsis is a critical organ malfunction that results from an abnormal response of the body to infection and might be lethal. The early detection of sepsis is essential for the patient's life. However, the traditional clinical diagnostic systems are not capable of analyzing the complicated changes in the patient's vitals over time. Therefore, a hybrid predictive framework that merges Light Gradient Boosting Machine (LightGBM) and Long Short-Term Memory (LSTM) networks for fast and accurate sepsis detection in real-time using freely accessible MIMIC-III data, has been proposed in this research. Using LightGBM, the nonlinear relationships among the features are learnt very fast and efficient, while the LSTM gives the temporal dependencies in the sequence of the patient vital signs. The combined output of the two models is said to be more sensitive and robust than that of the single models. A Streamlit-based clinical dashboard is being provided, allowing for real-time predictions and visualization for healthcare professionals. The proposed system has shown a considerable increase in the accuracy of early sepsis detection and offers a non-restricted method for AI-assisted ICU monitoring. 2025 IEEE. Subject The topic of the resource Hybrid Model; LightGBM; LSTM; MIMIC-III Dataset; Sepsis Prediction; Streamlit Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159484-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online