A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports

Abstract

Knowledge graph visualization in ultrasound reports is essential for enhancing medical decision making and the efficiency and accuracy of computer-aided analysis tools. This study aims to propose an intelligent method for analyzing ultrasound reports through knowledge graph visualization. Firstly, we provide a novel method for extracting key term networks from the narrative text in ultrasound reports with high accuracy, enabling the identification and annotation of clinical concepts within the report. Secondly, a knowledge representation framework based on ultrasound reports is proposed, which enables the structured and intuitive visualization of ultrasound report knowledge. Finally, we propose a knowledge graph completion model to address the lack of entities in physicians’ writing habits and improve the accuracy of visualizing ultrasound knowledge. In comparison to traditional methods, our proposed approach outperforms the extraction of knowledge from complex ultrasound reports, achieving a significantly higher extraction index (η) of 2.69, surpassing the general pattern-matching method (2.12). In comparison to other state-of-the-art methods, our approach achieves the highest P (0.85), R (0.89), and F1 (0.87) across three testing datasets. The proposed method can effectively utilize the knowledge embedded in ultrasound reports to obtain relevant clinical information and improve the accuracy of using ultrasound knowledge