Diagnostic accuracy plays a pivotal role in healthcare, directly affecting treatment efficacy and patient outcomes. Errors in diagnosis can result in inappropriate or delayed treatments, exacerbating conditions and es...
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Diagnostic accuracy plays a pivotal role in healthcare, directly affecting treatment efficacy and patient outcomes. Errors in diagnosis can result in inappropriate or delayed treatments, exacerbating conditions and escalating costs. Traditional diagnostic approaches often analyze data in isolation, which can lead to potential oversights and inefficiencies stemming from the fragmented nature of healthcare data systems. clinical knowledge graphs (CKGs) offer a solution by integrating diverse data types-including electronic health records, clinical notes, and genomic data-into a dynamic and interconnected framework, thereby facilitating a holistic understanding of patient health. However, CKGs encounter challenges such as maintaining current and accurate information, adapting to new data, and managing computational complexity, which can limit their effectiveness in real-time clinical applications. This paper proposes anew method that incorporates a dynamic graph attention mechanism within a CKG to enhance diagnostic accuracy in healthcare. By unifying various data sources within a graph-based framework and leveraging advanced graph learning techniques, the proposed method addresses the challenges associated with traditional diagnostic models. Comparative analyses demonstrate the model's superior performance in both accuracy and efficiency, highlighting its potential for clinical application.
Medical report generation is an important cross-modal task in the field of medicine, aiming to automatically generate professional and accurate reports for given medical images. Integrating clinicalknowledge into the...
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