Abstract
With the advent of medical big data era, medical knowledge graph has received extensive attention. The traditional knowledge graph prediction methods are mostly aimed at static data, which is not suitable for medical diagnostic data with dynamical variation characteristics. Take the example of pulmonary embolism in the clinical medicine domain, it is a typical high-risk lethal disease, and its course of disease has the characteristics of rapid deterioration over time. Therefore, it is necessary to consider the course of disease over time to predict the complications of pulmonary embolism and propose a reasonable diagnosis and treatment recommendation, it brings huge challenge to traditional knowledge graph prediction methods. For this reason, this paper proposes a deep learning method based on embedded representation, by using GRU to introduce temporal information into the knowledge graph and using TransR to ensure the structure property of the knowledge graph, to improve the accuracy and arithmetic performance of intelligent inference of the knowledge graph. The medically aided diagnosis system we have developed has been clinically implemented in several hospitals, and the effectiveness, reliability and stability of the system have been verified through practical application.
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Acknowledgment
The work is partially supported by the Fundamental Research Funds for the Central Universities (No. N171602003), and Liaoning Distinguished Professor (No. XLYC1902057).
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Song, F., Wang, B., Tang, Y., Sun, J. (2020). Research of Medical Aided Diagnosis System Based on Temporal Knowledge Graph. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham. https://doi.org/10.1007/978-3-030-65390-3_19
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