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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Huang, Z., Yang, J., van Harmelen, F., Hu, Q.: Constructing disease-centric knowledge graphs: a case study for depression (short version). In: Proceedings of the 2017 International Conference of Artificial Intelligence in Medicine (2017)
Ferrari, A., Somerville, A., Baxter, A., Norman, R., SB Patten, T.V., Whiteford, H.: Global variation in the prevalence and incidence of major depressive disorder: a systematic review of the epidemiological literature. Psychol. Med. 43(3), 471–481 (2013)
Cyganiak, R., Wood, D., Lanthaler, M.: RDF 1.1 concepts and abstract syntax (2014)
Goodwin, T., Harabagi, S.M.: Automatic generation of a qualified medical knowledge graph and its usage for retrieving patient cohorts from electronic medical records. In: IEEE Seventh International Conference on Semantic Computing (2013)
Panahiazar, M., Taslimitehrani, V., Jadhav, A., Pathak, J.: Empowering personalized medicine with big data and semantic web technology: Promises, challenges, and use cases. In: IEEE International Conference on Big Data (2014)
Zamborlini, V., Hoekstra, R., Silveira, M.D., Pruski, C., ten Teije, A., van Harmelen, F.: Inferring recommendation interactions in clinical guidelines. Semantic Web 7(4), 421–446 (2016)
Jovanovik, M., Trajanov, D.: Consolidating drug data on a global scale using linked data. J. Biomed. Semant. 8(1), 3 (2017)
Huang, Z., ten Teije, A., van Harmelen, F., Ait-Mokhtar, S.: Semantic representation of evidence-based clinical guidelines. In: Proceedings of 6th International Workshop on Knowledge Representation for Health Care (KR4HC 2014) (2014)
Huang, Z., van Harmelen, F., ten Teije, A., Dentler, K.: Knowledge-based patient data generation. In: Riaño, D., Lenz, R., Miksch, S., Peleg, M., Reichert, M., ten Teije, A. (eds.) KR4HC/ProHealth -2013. LNCS, vol. 8268, pp. 83–96. Springer, Cham (2013). doi:10.1007/978-3-319-03916-9_7
Ait-Mokhtar, S., Bruijn, B.D., Hagege, C., Rupi, P.: Intermediary-stage ie components, D3.5. Technical report, EURECA Project (2014)
Khiari, A.: Identification of variants of compound terms, master thesis. Technical report, Universit Paul Sabatier, Toulouse (2015)
Aronson, A.R., Lang, F.: An overview of metamap: historical perspective and recent advances. J. Am. Med. Inform. Assoc.: JAMIA 17(3), 229–236 (2010)
Huang, Z., ten Teije, A., van Harmelen, F.: SemanticCT: a semantically-enabled system for clinical trials. In: Riaño, D., Lenz, R., Miksch, S., Peleg, M., Reichert, M., ten Teije, A. (eds.) KR4HC/ProHealth -2013. LNCS, vol. 8268, pp. 11–25. Springer, Cham (2013). doi:10.1007/978-3-319-03916-9_2
Fensel, D., van Harmelen, F., Andersson, B., Brennan, P., Cunningham, H., Della Valle, E., Fischer, F., Huang, Z., Kiryakov, A., Lee, T., School, L., Tresp, V., Wesner, S., Witbrock, M., Zhong, N.: Towards LarKC: a platform for web-scale reasoning. In: Proceedings of the IEEE International Conference on Semantic Computing (ICSC 2008). IEEE Computer Society Press, CA (2008)
Huang, Z., den Teije, A., van Harmelen, F.: Rule-based formalization of eligibility criteria for clinical trials. In: Proceedings of the 14th Conference on Artificial Intelligence in Medicine (AIME 2013) (2013)
Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semantic Web 8(3), 489–508 (2017)
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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