BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Selma Tabakovic (Chalmers University of Technology & University of Gothenburg) DTSTART:20240313T121500Z DTEND:20240313T130000Z DTSTAMP:20260422T155052Z UID:gbgstats/46 DESCRIPTION:Title: AI-driven sepsis care: early detection and personalized treatment\nb y Selma Tabakovic (Chalmers University of Technology & University of Gothe nburg) as part of Gothenburg statistics seminar\n\nLecture held in MVL14.\ n\nAbstract\nSepsis is a life-threatening organ dysfunction caused by a dy sregulated host response to infection\, and remains a leading cause of dea th in intensive care units worldwide. An optimal treatment strategy is sti ll unknown\, leading to a significant variability in sepsis treatment with poorer outcomes.\n\nRecently\, deep reinforcement learning has shown prom ise as a decision-aiding tool for the administration of intravenous fluids and vasopressors to septic patients. However\, these models are limited i n their ability to accommodate the entire range from high-risk to low-risk patients\, and thus fail to provide personalized treatment recommendation s.\n\nTo address this limitation\, in particular in the presence of hetero geneous patient groups or heterogeneous treatment responses\, we propose a Multi-Head Dueling Double Deep Q-Network (MH-DQN) model that incorporates patient characteristics to enable more personalized treatment recommendat ions. The MH-DQN model has multiple output layers\, each of which is optim ized for a specific patient profile. The model is trained using the Medica l Information Mart for Intensive Care (MIMIC-III) database.\n LOCATION:https://researchseminars.org/talk/gbgstats/46/ END:VEVENT END:VCALENDAR