@InProceedings{ling-an-hasan:2017:SENSE2017,
  author    = {Ling, Yuan  and  An, Yuan  and  Hasan, Sadid},
  title     = {Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge},
  booktitle = {Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {31--36},
  abstract  = {This paper presents a novel approach to the task of automatically inferring the
	most probable diagnosis from a given clinical narrative. Structured Knowledge
	Bases (KBs) can be useful for such complex tasks but not sufficient. Hence, we
	leverage a vast amount of unstructured free text to integrate with structured
	KBs. The key innovative ideas include building a concept graph from both
	structured and unstructured knowledge sources and ranking the diagnosis
	concepts using the enhanced word embedding vectors learned from integrated
	sources. Experiments on the TREC CDS and HumanDx datasets showed that our
	methods improved the results of clinical diagnosis inference.},
  url       = {http://www.aclweb.org/anthology/W17-1904}
}