@inbook{0a3a2ec2685448339a2ca93455e0e36f,
  title     = "Constructing Knowledge Graphs of Depression",
  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].).",
  author    = "Zhisheng Huang and Jie Yang and {van Harmelen}, Frank and Qing Hu",
  year      = "2017",
  doi       = "10.1007/978-3-319-69182-4_16",
  isbn      = "9783319691817",
  volume    = "10594 LNCS",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "149--161",
  booktitle = "Health Information Science - 6th International Conference, HIS 2017, Proceedings",
}

{"_id":"BYvPtEHeorJWiDx2a","bibbaseid":"huang-yang-vanharmelen-hu-constructingknowledgegraphsofdepression-2017","downloads":0,"creationDate":"2018-01-22T13:16:33.838Z","title":"Constructing Knowledge Graphs of Depression","author_short":["Huang, Z.","Yang, J.","van Harmelen , F.","Hu, Q."],"year":2017,"bibtype":"inbook","biburl":"https://raw.githubusercontent.com/KRRVU/website/master/publications/krr.bib","bibdata":{"bibtype":"inbook","type":"inbook","title":"Constructing Knowledge Graphs of Depression","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].).","author":[{"firstnames":["Zhisheng"],"propositions":[],"lastnames":["Huang"],"suffixes":[]},{"firstnames":["Jie"],"propositions":[],"lastnames":["Yang"],"suffixes":[]},{"propositions":["van Harmelen"],"lastnames":[],"firstnames":["Frank"],"suffixes":[]},{"firstnames":["Qing"],"propositions":[],"lastnames":["Hu"],"suffixes":[]}],"year":"2017","doi":"10.1007/978-3-319-69182-4_16","isbn":"9783319691817","volume":"10594 LNCS","series":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","publisher":"Springer/Verlag","pages":"149–161","booktitle":"Health Information Science - 6th International Conference, HIS 2017, Proceedings","bibtex":"@inbook{0a3a2ec2685448339a2ca93455e0e36f,\n title = \"Constructing Knowledge Graphs of Depression\",\n 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].).\",\n author = \"Zhisheng Huang and Jie Yang and {van Harmelen}, Frank and Qing Hu\",\n year = \"2017\",\n doi = \"10.1007/978-3-319-69182-4_16\",\n isbn = \"9783319691817\",\n volume = \"10594 LNCS\",\n series = \"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\",\n publisher = \"Springer/Verlag\",\n pages = \"149--161\",\n booktitle = \"Health Information Science - 6th International Conference, HIS 2017, Proceedings\",\n}\n\n\n","author_short":["Huang, Z.","Yang, J.","van Harmelen , F.","Hu, Q."],"key":"0a3a2ec2685448339a2ca93455e0e36f","id":"0a3a2ec2685448339a2ca93455e0e36f","bibbaseid":"huang-yang-vanharmelen-hu-constructingknowledgegraphsofdepression-2017","role":"author","urls":{},"metadata":{"authorlinks":{}}},"search_terms":["constructing","knowledge","graphs","depression","huang","yang","van harmelen ","hu"],"keywords":[],"authorIDs":[],"dataSources":["H6xuGqu5uQ6rXhdJ4","dJmTXpbSWWjnxatYT"]}