title: Applying MetaMap to Medline for identifying novel associations in a large clinical dataset: a feasibility analysis.
abstract: OBJECTIVE
  We describe experiments designed to determine the feasibility of distinguishing known from novel associations based on a clinical dataset comprised of International Classification of Disease, V.9 (ICD-9) codes from 1.6 million patients by comparing them to associations of ICD-9 codes derived from 20.5 million Medline citations processed using MetaMap. Associations appearing only in the clinical dataset, but not in Medline citations, are potentially novel.
  
  
  METHODS
  Pairwise associations of ICD-9 codes were independently identified in both the clinical and Medline datasets, which were then compared to quantify their degree of overlap. We also performed a manual review of a subset of the associations to validate how well MetaMap performed in identifying diagnoses mentioned in Medline citations that formed the basis of the Medline associations.
  
  
  RESULTS
  The overlap of associations based on ICD-9 codes in the clinical and Medline datasets was low: only 6.6% of the 3.1 million associations found in the clinical dataset were also present in the Medline dataset. Further, a manual review of a subset of the associations that appeared in both datasets revealed that co-occurring diagnoses from Medline citations do not always represent clinically meaningful associations.
  
  
  CONCLUSIONS
  Identifying novel associations derived from large clinical datasets remains challenging. Medline as a sole data source for existing knowledge may not be adequate to filter out widely known associations.
  
  
  CONCLUSIONS
  In this study, novel associations were not readily identified. Further improvements in accuracy and relevance for tools such as MetaMap are needed to realize their expected utility.
authors.affiliation: Department of Pediatrics, University of Michigan Medical School, Ann Arbor, Michigan, USA.
authors.forename: David A
authors.initials: DA
authors.lastname: Hanauer
authors.affiliation: Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA.
authors.forename: Mohammed
authors.initials: M
authors.lastname: Saeed
authors.affiliation: Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, Michigan, USA School of Information, University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: Kai
authors.initials: K
authors.lastname: Zheng
authors.affiliation: School of Information, University of Michigan, Ann Arbor, Michigan, USA Department of Electronic Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: Qiaozhu
authors.initials: Q
authors.lastname: Mei
authors.affiliation: Center for Statistical Consultation and Research, University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: Kerby
authors.initials: K
authors.lastname: Shedden
authors.affiliation: Lister Hill Center, National Library of Medicine, Bethesda, Maryland, USA.
authors.forename: Alan R
authors.initials: AR
authors.lastname: Aronson
authors.affiliation: Department of Computer Science, Discovery Analytics Center, Virginia Tech, Arlington, Virginia, USA.
authors.forename: Naren
authors.initials: N
authors.lastname: Ramakrishnan
mesh_terms: D057225:Data Mining; D005240:Feasibility Studies; D006801:Humans; D038801:International Classification of Diseases; D016239:MEDLINE; D009323:Natural Language Processing; D017432:Unified Medical Language System
keywords: Data Mining; Electronic Health Records; International Classification of Diseases; Medline; Natural Language Processing; Unified Medical Language System