Study on Dynamic Technology Opportunity Detection Method Based on Graph Neural Network -- A Case Study of Diagnosis and Treatment of Atherosclerosis

Corresponding author: Tang Xiaoli, tang.xiaoli@imicams.ac.cn

DOI: 10.12201/bmr.202503.00040

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  Abstract: Purpose/Significance In view of the problem that the existing technical opportunity discovery methods rely on static networks and it is difficult to capture the dynamic evolution law, a method of technical opportunity prediction based on dynamic graph neural network is proposed, aiming to provide intelligent decision support for the innovation of atheromatosis-related technologies. Methods/Process The patent data in the field of atherosclerosis from 2004 to 2024 were obtained from Incopat, the technical elements were extracted, and a dynamic technical semantic network containing the technical elements was constructed. A DGTec-Opp dynamic graph neural network model was proposed, and the atherosclerotic patent data was divided by sliding time window. The performance of the model is verified by comparative experiments. Result/Conclusion Experimental results show that the AUC ROC of DGTec-Opp is 0.932 and Accuracy@10 is 62.3%, which is improved in different degrees compared with the other three baseline models, and can provide reliable prediction support for technical opportunity discovery.

  Key words: technology opportunity discovery; dynamic graph neural network; technical semantic network; evolutionary theory of technology combination

  Submit time: 14 March 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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