Abstract
Knowledge Graphs have been shown to be useful tools for integrating multiple medical knowledge sources, and to support such tasks as medical decision making, literature retrieval, determining healthcare quality indicators, co-morbodity analysis and many others. A large number of medical knowledge sources have by now been converted to knowledge graphs, covering everything from drugs to trials and from vocabularies to gene-disease associations. Such knowledge graphs have typically been generic, covering very large areas of medicine. (e.g. all of internal medicine, or arbitrary drugs, arbitrary trials, etc.). This has had the effect that such knowledge graphs become prohibitively large, hampering both efficiency for machines and usability for people. In this paper we show how we use 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 (This paper is an extended version of [1].).
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Acknowledgments
This work is partially supported by the Dutch national project COMMIT/Data2Semantics, the major 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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Huang, Z., Yang, J., van Harmelen, F., Hu, Q. (2017). Constructing Knowledge Graphs of Depression. In: Siuly, S., et al. Health Information Science. HIS 2017. Lecture Notes in Computer Science(), vol 10594. Springer, Cham. https://doi.org/10.1007/978-3-319-69182-4_16
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DOI: https://doi.org/10.1007/978-3-319-69182-4_16
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