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 Parashar, Jyoti; Upreti, Kamal; Kshirsagar, Pravin R.; Hung, Bui Thanh Title A name given to the resource Reinforcement Learning for Early Detection and Intervention of Sepsis with Graph-Based Personalized Treatment Recommendations 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 Lecture Notes in Electrical Engineering;Volume;1386 LNEE;pp.131-141 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-3936-6_11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-3936-6_11</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015643385?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015643385?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 Parashar J., Dr. Akhilesh Das Gupta, Institute of Professional Studies, Delhi, India; Upreti K., CHRIST (Deemed to Be University), Delhi, NCR, India; Kshirsagar P.R., J D College of Engineering &amp; Management, Maharashtra, Nagpur, India; Hung B.T., Data Science Department, Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Viet Nam Description An account of the resource Early detection and treatment of sepsis, a condition that can become fatal through the bodys response to infection, can enhance patient life. This paper explores how reinforcement learning can be applied to the early detection and treatment of sepsis, along with its novel features, which include personalized treatment recommendations and graph-based representations using Graph Neural Networks (GNNs). Moreover, domain adaptation and transfer learning strategies make the model applicable in a wide range of clinical contexts. The RL model is therefore designed to identify early warning signs and give prompt, individualized answers to avoid major repercussions. To ensure wide application, the RL model was trained using an enormous dataset of patient vitals, lab results, and clinical notes from numerous centers. It is already proven in real-life clinical situations that this model can improve patient outcomes and the quality of clinical decisions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Domain adaption; Graph neural network (GNN); Reinforcement learning (RL); Sepsis detection; Transfer learning Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18761100; ISBN: 978-981963935-9; 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