TY - GEN
T1 - Constructing Knowledge Graphs of Depression
AU - Huang, Zhisheng
AU - Yang, Jie
AU - van Harmelen, Frank
AU - Hu, Qing
PY - 2017
Y1 - 2017
N2 - 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].).
AB - 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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UR - http://www.scopus.com/inward/citedby.url?scp=85032506142&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-69182-4_16
DO - 10.1007/978-3-319-69182-4_16
M3 - Conference contribution
AN - SCOPUS:85032506142
SN - 9783319691817
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 149
EP - 161
BT - Health Information Science
A2 - Siuly, Siuly
A2 - Huang, Zhisheng
A2 - Aickelin, Uwe
A2 - Zhou, Rui
A2 - Wang, Hua
A2 - Zhang, Yanchung
A2 - Klimenko, Stanislav
PB - Springer/Verlag
T2 - 6th International Conference on Health Information Science, HIS 2017
Y2 - 7 October 2017 through 9 October 2017
ER -
