Description
| Title | Constructing Disease-centric Knowledge Graphs: a case study for depression |
| Abstract | A large number of medical knowledge sources have been converted to knowledge graphs, covering everything from drugs to trials and from vocabularies to gene-disease associations. Such knowledge graphs are typically generic, covering very large areas of medicine (e.g. all of internal medicine, or arbitrary drugs, arbitrary trials, etc). Such knowledge graphs become prohibitively large, hampering both efficiency for machines and usability for people. In this talk we show how we used 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. |
Other presentations by Zhisheng Huang
| Date | Title |
|---|---|
| 13 November 2006 | |
| 07 January 2008 | |
| 24 November 2008 | Ontology Versioning and Effect Space |
| 05 October 2009 | Evaluation and Benchmarking of Ontology Reasoners |
| 03 October 2011 | A Hybrid Spatial Logic for Geodetic Reasoning |
| 21 January 2013 | SemanticCT: A Semantically-Enabled System for Clinical Trials |
| 18 November 2013 | Knowledge-based Patient Data Generation |
| 01 September 2014 | Feasibility Estimation for Clinical Trials |
| 30 March 2015 | Identifying Evidence Quality for Updating Evidence-based Medical Guidelines |
| 07 March 2016 | Semantic Processing of Medical Text with NLP tools |
| 19 December 2016 | Smart Ward |
| 22 May 2017 | Constructing Disease-centric Knowledge Graphs: a case study for depression |
| 17 September 2018 | Semantic Technology for Research Data Search |