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

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].).

Original languageEnglish
Title of host publicationHealth Information Science - 6th International Conference, HIS 2017, Proceedings
PublisherSpringer/Verlag
Pages149-161
Number of pages13
Volume10594 LNCS
ISBN (Print)9783319691817
DOIs
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

Conference

Conference6th International Conference on Health Information Science, HIS 2017
CountryRussian Federation
CityMoscow
Period7/10/179/10/17

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