Knowledge Graph-Based Clinical Decision Support

Abstract

Healthcare data is rapidly increasing in volume and complexity, making clinical decision-making more challenging for practitioners. Traditional machine learning approaches often fail to capture the semantic relationships among clinical entities such as symptoms, diagnoses, medications, and patient history. To overcome this limitation, this paper presents a Knowledge Graph-Based Clinical Decision Support System (KG-CDSS) that integrates heterogeneous healthcare data into a unified graph structure. The proposed system utilizes ontology-driven modeling to represent medical knowledge and employs graph-based inference mechanisms to support accurate diagnosis and treatment recommendations. By leveraging relationships between clinical concepts, the system enhances interpretability and provides context-aware decision support. Experimental observations indicate that the proposed approach improves prediction accuracy and supports personalized healthcare compared to conventional models. Additionally, the integration of prior data mining and ensemble learning techniques strengthens the robustness of the system. This study demonstrates that knowledge graph-based approaches can effectively bridge the gap between data-driven models and clinical reasoning, making them suitable for real-world healthcare applications, especially in resource-constrained environments.