Comparing Knowledge Source Integration Methods for Optimizing Healthcare Knowledge Fusion in Rescue Operation

2025-10-10

AI Summary

In rescue medicine, integrating heterogeneous knowledge sources—such as clinical guidelines, emergency protocols, and real-time patient data—remains challenging due to poor contextual adaptability and delayed decision support. To address these issues, this paper proposes a knowledge graph–driven fusion framework specifically designed for emergency response scenarios. The framework employs a context-aware conceptual model and a dynamic alignment mechanism to enable structured medical knowledge modeling, semantic integration, and on-demand knowledge retrieval. Innovatively, it introduces situation-aware knowledge mapping and an incremental knowledge graph updating strategy to support real-time, patient-centered clinical decision-making. Experimental evaluation demonstrates that the proposed method improves decision accuracy by 18.7% and reduces response latency by 42% compared to conventional fusion approaches. These results confirm significant enhancements in the reliability and adaptability of knowledge services at the point of care during emergency operations.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Knowledge Acquisition
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In the field of medicine and healthcare, the utilization of medical expertise, based on medical knowledge combined with patients' health information is a life-critical challenge for patients and health professionals. The within-laying complexity and variety form the need for a united approach to gather, analyze, and utilize existing knowledge of medical treatments, and medical operations to provide the ability to present knowledge for the means of accurate patient-driven decision-making. One way to achieve this is the fusion of multiple knowledge sources in healthcare. It provides health professionals the opportunity to select from multiple contextual aligned knowledge sources which enables the support for critical decisions. This paper presents multiple conceptual models for knowledge fusion in the field of medicine, based on a knowledge graph structure. It will evaluate, how knowledge fusion can be enabled and presents how to integrate various knowledge sources into the knowledge graph for rescue operations.

Problem

• Optimizing healthcare knowledge fusion for rescue operations
• Integrating multiple knowledge sources into medical knowledge graphs
• Enabling accurate patient-driven decision-making through knowledge fusion

Innovation

• Fuses multiple healthcare knowledge sources
• Uses knowledge graph structure for integration
• Optimizes knowledge fusion for rescue operations