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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zenglin, X., Sheng, Y., He, L., Wang, Y.: Review on knowledge graph techniques. J. Univ. Electron. Sci. Technol. 45(4), 589–606 (2016)
Pujara, J., Miao, H., Getoor, L., Cohen, W.: Knowledge graph identification. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 542–557. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41335-3_34
Sulakhe, D., et al.: Lynx: a database and knowledge extraction engine for integrative medicine. Nucleic Acids Res. 42(D1), D1007–D1012 (2014)
Jia, L., et al.: Construction of traditional Chinese medicine knowledge graph. J. Med. Inf. (8), 51–53 (2015)
Yang, X., Wang, B., Yang, K., Liu, C., Zheng, B.: A novel representation and compression for queries on trajectories in road networks (extended abstract). In 35th IEEE International Conference on Data Engineering, ICDE 2019, Macao, China, 8–11 April 2019, pp. 2117–2118. IEEE (2019)
Li, X., Liu, Y., He, L., Liu, B., Zhang, Y.: Research review of knowledge graph and its application in TCM field. Chin. J. Inf. Tradition. Chin. Med. 24(7), 129–132 (2017)
Baader, F., Sertkaya, B.: Usability issues in description logic knowledge base completion. In: Ferré, S., Rudolph, S. (eds.) ICFCA 2009. LNCS (LNAI), vol. 5548, pp. 1–21. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01815-2_1
Yang, X., Li, C.: Secure XML publishing without information leakage in the presence of data inference. In: Nascimento, M.A., Tamer Özsu, M., Kossmann, D., Miller, R.J., Blakeley, J.A., Bernhard Schiefer, K. (eds.) Proceedings of the Thirtieth International Conference on Very Large Data Bases, VLDB 2004, Toronto, Canada, 31 August 31–3 September 2004, pp. 96–107. Morgan Kaufmann (2004)
Liu, Z., Sun, M., Lin, Y., Xie, R.: Knowledge representation learning: a review. J. Comput. Res. Dev. 53(2), 247–261 (2016)
Bordes, A., Usunier, N., GarcÃa-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Burges, C.J.C., Bottou, L., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held 5–8 December 2013, Lake Tahoe, Nevada, United States, pp. 2787–2795 (2013)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Brodley, C.E., Stone, P. (eds.) Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, Québec City, Québec, Canada, 27–31 July 2014, pp. 1112–1119. AAAI Press (2014)
Dai, S., Liang, Y., Liu, S., Wang, Y., Shao, W.: Learning entity and relation embeddings with entity description for knowledge graph completion. In: Proceedings of 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications, pp. 202–205 (2018)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Burgard, W., Roth, D. (eds.) Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2011, San Francisco, California, USA, 7–11 August 2011. AAAI Press (2011)
Yang, X., Wang, Y., Wang, B., Wang, W.: Local filtering: improving the performance of approximate queries on string collections. In: Sellis, T.K., Davidson, S.B., Ives, Z.C. (eds.) Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, 31 May–4 June 2015, pp. 377–392. ACM (2015)
Socher, R., Chen, D., Manning, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion. In: Burges, C.J.C., Bottou, L., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held 5–8 December 2013, Lake Tahoe, Nevada, United States, pp. 926–934 (2013)
Bordes, A., Glorot, X., Weston, J., Bengio, Y.: A semantic matching energy function for learning with multi-relational data - application to word-sense disambiguation. Mach. Learn. 94(2), 233–259 (2014)
Lecun, Y., Chopra, S., Ranzato, MM.A., Huang, F.J.: A tutorial on energy-based learning. Raia Hadsell (2006)
Lin, T.-Y., RoyChowdhury, A., Maji, S.: Bilinear CNN models for fine-grained visual recognition. In: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, 7–13 December 2015, pp. 1449–1457. IEEE Computer Society (2015)
Scarselli, F., Gori, M., Chung Tsoi, A., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2009)
Leblay, J., Chekol, .W.: Deriving validity time in knowledge graph. In: Champin, P.-A., Gandon, F.L., Lalmas, M., Ipeirotis, P.G. (eds.) Companion of the The Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon, France, 23–27 April 2018, pp. 1771–1776. ACM (2018)
Chekol, M.W., Pirrò, G., Schoenfisch, J., Stuckenschmidt, H.: Marrying uncertainty and time in knowledge graphs. In: Singh, S.P., Markovitch, S., (eds.) Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, California, USA, 4–9 February 2017, pp. 88–94. AAAI Press (2017)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation, pp. 1724–1734. ACL (2014)
Ji, G., He, S., Xu, L., Kang, L., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In Meeting of the Association for Computational Linguistics International Joint Conference on Natural Language Processing (2015)
Acknowledgment
The work is partially supported by the Fundamental Research Funds for the Central Universities (No. N171602003), and Liaoning Distinguished Professor (No. XLYC1902057).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-65390-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65389-7
Online ISBN: 978-3-030-65390-3
eBook Packages: Computer ScienceComputer Science (R0)