Constructing Knowledge Graphs of Depression

Zhisheng Huang*, Jie Yang, Frank van Harmelen, Qing Hu

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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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].).

Original languageEnglish
Title of host publicationHealth Information Science
Subtitle of host publication6th International Conference, HIS 2017, Moscow, Russia, October 7-9, 2017, Proceedings
EditorsSiuly Siuly, Zhisheng Huang, Uwe Aickelin, Rui Zhou, Hua Wang, Yanchung Zhang, Stanislav Klimenko
Number of pages13
ISBN (Electronic)9783319691824
ISBN (Print)9783319691817
Publication statusPublished - 2017
Event6th International Conference on Health Information Science, HIS 2017 - Moscow, Russian Federation
Duration: 7 Oct 20179 Oct 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10594 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference6th International Conference on Health Information Science, HIS 2017
Country/TerritoryRussian Federation


The DepressionKG system supports knowledge browsing and querying. A screenshot of the interface of the DepressionKG system is shown in Fig. 3. That screenshot also shows the result of a semantic query about the side-effects of antidepressants with respect to weight change. It will be deployed and evaluated in Beijing Anding Hospital, one of the biggest psychiatric hospitals in China, for experiments in the Smart Ward project, a case study in a Major International Cooperation Project between Beijing University of Technology (BJUT) and VU University Amsterdam, funded by the National Natural Science Foundation of China (2015-2019). The objective of the Smart Ward project is to develop a knowledge-based platform for monitoring and analyzing the status of patients and for supporting clinical decision making in a psychiatric ward. Therefore, we provided a bi-linguistic (English and Chinese) interface to the DepressionKG system. The deployment of the DepressionKG system in the Smart Ward project will be systematically evaluated in Beijing Anding Hospital, using realistic clinical scenarios from the hospital. 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.

FundersFunder number
Beijing Anding Hospital
Dutch national project COMMIT/Data2Semantics61420106005
National Natural Science Foundation of China2015-2019
Vrije Universiteit Amsterdam
Beijing University of Technology
China Scholarship Council


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