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Abstract
In this paper we show how we used multiple large knowledge sources to construct a much smaller knowledge graph that is focussed on single disease (in our case major depression disorder). Such a disease-centric knowledge-graph makes it more convenient for doctors (in our case psychiatric doctors) to explore the relationship among various knowledge resources and to answer realistic clinical queries.
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References
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Acknowledgments
This work is partially supported by the Dutch national project COMMIT, the international cooperation project No. 61420106005 funded by National Natural Science Foundation of China, and the NWO-funded Project Re-Search. The fourth author is funded by the China Scholarship Council.
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Authors and Affiliations
VU University Amsterdam, Amsterdam, The Netherlands
Zhisheng Huang, Frank van Harmelen & Qing Hu
Beijing Anding Hospital, Beijing, China
Jie Yang
College of Computer Science and Technology, Wuhan Univesity of Science and Technology, Wuhan, China
Qing Hu
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- Frank van Harmelen
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Correspondence toZhisheng Huang.
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Editors and Affiliations
Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Annette ten Teije
Medical University of Vienna, Vienna, Austria
Christian Popow
University of Pennsylvania, Philadelphia, Pennsylvania, USA
John H. Holmes
University of Pavia, Pavia, Italy
Lucia Sacchi
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Huang, Z., Yang, J., van Harmelen, F., Hu, Q. (2017). Constructing Disease-Centric Knowledge Graphs: A Case Study for Depression (short Version). In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_5
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