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An Enhanced Semantic Framework for Time-Constrained Clinical Decision-Making in Emergency Settings

Authors:
Sivan Albagli-Kim
Dizza Beimel

Keywords: knowledge graph; semantic reasoning; decision support systems; semantic technology

Abstract:
Rapid and accurate decision-making is essential for identifying and treating life-threatening conditions in emergency medicine. This paper presents an enhancement to an existing Knowledge Graph-based clinical decision-making framework by integrating an emergency strategy layer to prioritize critical diagnoses. By categorizing diseases as life-threatening or non-life-threatening, our approach emphasizes the immediate exclusion of high-risk conditions. The enhancement is manifested on two primary levels: (a) we augmented the KG by incorporating conditional edges that are dynamically activated based on patient-specific indicators, such as age, gender, and pre-existing conditions. These conditional edges allow the framework to adapt to individual patient profiles, supporting a more precise and personalized diagnostic process; and (b) we refined the framework’s algorithms to prioritize excluding life-threatening diseases. Future work will evaluate the framework with real-world clinical data and expand the KG’s logic to include continuous data, further enhancing inference accuracy. Our contribution provides a foundation for expanding clinical decision-making frameworks to address urgent clinical needs, potentially improving patient outcomes in critical medical scenarios.

Pages: 65 to 71

Copyright: Copyright (c) IARIA, 2025

Publication date: March 9, 2025

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-68558-244-9

Location: Lisbon, Portugal

Dates: from March 9, 2025 to March 13, 2025