Abstract: This study presents a novel approach to evaluate the efficacy of JingFang granules in treating influenza-like illness by integrating knowledge graph technology with clinical trial data. We developed an innovative knowledge graph-based pharmacological analysis method and validated its effectiveness through a randomized controlled clinical trial. A knowledge graph was constructed by extracting drug-disease entities and their relationships from the literature using a machine learning workflow. Deep mining of the knowledge graph was performed using a graph convolutional network and T5 mini-model to analyze the association between JingFang and various diseases. Subsequently, a randomized controlled clinical trial involving 106 patients was conducted. Results showed that the cure rate in the JingFang combined treatment group (92.5%) was significantly higher than in the control group (81.1%), especially among the middle-aged and elderly population. Subgroup analysis revealed that JingFang had a more pronounced therapeutic effect on patients aged 34 and above, consistent with the knowledge graph analysis results. The innovation of this study lies in proposing a novel framework for evaluating therapeutic efficacy by combining knowledge graphs with clinical trial results. This approach not only provides new analytical tools for similar drug development but also improves the efficiency and accuracy of drug development by systematically validating literature efficacy data and integrating it with actual clinical trial results. Furthermore, applying a knowledge graph to evaluate the therapeutic effects of traditional Chinese medicines like JingFang is an innovative and unique approach, bringing new perspectives to this under-explored field. This method holds potential for broad application in drug development and repurposing, particularly in the context of Traditional Chinese Medicine.

Yuqing Li, Zhitao Jiang, Zhiyan Huang, Wenqiao Gong, Yanling Jiang and Guoliang Cheng. “Knowledge Graph-Based JingFang Granules Efficacy Analysis for Influenza-Like Illness”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.01507105

@article{Li2024,
title = {Knowledge Graph-Based JingFang Granules Efficacy Analysis for Influenza-Like Illness},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01507105},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01507105},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Yuqing Li and Zhitao Jiang and Zhiyan Huang and Wenqiao Gong and Yanling Jiang and Guoliang Cheng}
}