Testing Data: UMLS-Core

This list of terms was used in UMLS-Core projects. It is used as gold standard in this project for testing.

I. General Information

• UMLS-Core: SCTMap_withCUI_201302 (provided by Dr. K.W. Fung)
• In MS Excel Format:

  Term Id

  Local Term

  SNOMED CID

  SNOMED FSN

  UMLS CUI

• Contains 15,487 terms with valid mapped CUI (used as gold standard)
• Contains 13,077 unique terms
• 1,492 terms are duplicated with different ID (sources)
• 35 terms have multiple CUIs (ambiguous)

II. Data Process

• Convert from Excel to CVS format
• Convert from CVS to pipe separate format (gov.nih.nlm.nls.stmt.Lib.FromCsvToPipeFile)
• Filter out duplicated terms to unify term|CUI

• For testing input: Retrieve fields 2

  Local Term

• For gold standard: Retrieve fields 2,5

  Local Term

  UMLS CUI

III. Source of UMLS-Core data

• Problem list terminologies (local terms) from 6 (8) institutions
  • HA: Hong Kong Hospital Authority
  • IH: Intermountain Healthcare
  • KP: Kaiser Permanente
  • MA: Mayo Clinic
  • NU: University of Nebraska Medical Center
  • RI: Regenstrief Institute
• A problem list is a complete list of all patient's problem
• The data in the original paper:
  • 76,237 terms and their usage frequenies in 14 million patients were submitted from six institutions
  • 65,678 terms unique across instutions
  • mapping from the local problem list terms to standard terminologies (ICD-9-CM, SNOMED CT) if available
  • 14,395 terms covered 95% of usage in each institution (10,081 terms unique across institutions)
  • 13,26 terms were successfully mapped to 6,776 UMLS concepts
    • UMLS mapping - 2008AA: 10,812 (75%)
      • exact match - case-insensitive: 8,102 (56%)
      • normalized match: 2,035 (14%)
      • synonym substitution: 576 (5%)

    • local maps to standard terminilogies: 1,007 (7%)
      • automatically map - if labeled as exact match
      • manully reviewed for exact match - if not labeled as exact match

    • manual mapping use RRF browser: 1,442 (10%)

    • unmapped: 1,134 (8%)
      • Highly specific: 53%
      • Very general: 11%
      • Administrative: 7%
      • Laterality: 7%
      • Negative finding: 3%
      • Composit comcept: 3%
      • Meaning unclear (ambiguous): 2%
      • Miscellaneous: 13%
• References: UMLS-Core Project