A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports
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, w...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/8/560 |
| _version_ | 1797381900236488704 |
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| author | Jiayi Feng Runtong Zhang Donghua Chen Lei Shi |
| author_facet | Jiayi Feng Runtong Zhang Donghua Chen Lei Shi |
| author_sort | Jiayi Feng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T20:58:11Z |
| format | Article |
| id | doaj.art-dd8b3dc4456f47deae5f3ad5849144a0 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-08T20:58:11Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-dd8b3dc4456f47deae5f3ad5849144a02023-12-22T13:55:28ZengMDPI AGBiomimetics2313-76732023-11-018856010.3390/biomimetics8080560A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound ReportsJiayi Feng0Runtong Zhang1Donghua Chen2Lei Shi3Department of Information Management, Beijing Jiaotong University, Beijing 100044, ChinaDepartment of Information Management, Beijing Jiaotong University, Beijing 100044, ChinaDepartment of Information Management, University of International Business and Economics, Beijing 100029, ChinaSchool of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UKKnowledge 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.https://www.mdpi.com/2313-7673/8/8/560ultrasound reportknowledge graphknowledge representationmachine learningnatural language processingprecision medicine |
| spellingShingle | Jiayi Feng Runtong Zhang Donghua Chen Lei Shi A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports Biomimetics ultrasound report knowledge graph knowledge representation machine learning natural language processing precision medicine |
| title | A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports |
| title_full | A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports |
| title_fullStr | A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports |
| title_full_unstemmed | A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports |
| title_short | A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports |
| title_sort | visualization method of knowledge graphs for the computation and comprehension of ultrasound reports |
| topic | ultrasound report knowledge graph knowledge representation machine learning natural language processing precision medicine |
| url | https://www.mdpi.com/2313-7673/8/8/560 |
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