Reinforcement Learning for Early Detection and Intervention of Sepsis with Graph-Based Personalized Treatment Recommendations
- Title
- Reinforcement Learning for Early Detection and Intervention of Sepsis with Graph-Based Personalized Treatment Recommendations
- Creator
- Parashar, Jyoti; Upreti, Kamal; Kshirsagar, Pravin R.; Hung, Bui Thanh
- Description
- 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.
- Source
- Lecture Notes in Electrical Engineering;Volume;1386 LNEE;pp.131-141
- Date
- 01-01-2025
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Domain adaption; Graph neural network (GNN); Reinforcement learning (RL); Sepsis detection; Transfer learning
- Coverage
- 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 & 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
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 18761100; ISBN: 978-981963935-9;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Parashar, Jyoti; Upreti, Kamal; Kshirsagar, Pravin R.; Hung, Bui Thanh, “Reinforcement Learning for Early Detection and Intervention of Sepsis with Graph-Based Personalized Treatment Recommendations,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25518.