Using SNOMED CT with the UMLS - Medical Ontology Research

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Semantic Network • Semantic types (133) ◦ tree structure ◦ 2 major hierarchies • Entity • Physical Object • Conceptual Entity • Event • Activity • Phenomenon or Process
Semantic Network • Semantic network ◦ 54 relationships ◦ 603 asserted relations ◦ 6101 inferred relations • Asserted semantic network relations (603) ◦ hierarchical (isa = is a kind of) • among types (133) • Animal isa Organism • Enzyme isa Biologically Active Substance • among relations (54) • treats isa affects ◦ non-hierarchical (416) • Sign or Symptom diagnoses Pathologic Function • Pharmacologic Substance treats Pathologic Function
Page 1 and 2: Using SNOMED CT with the UMLS IHTSD
Page 3 and 4: Outline • Overview of the UMLS
Page 5 and 6: Overview of the UMLS Introduction
Page 7 and 8: Motivation • Started in 1986 •
Page 9 and 10: Overview of the UMLS Overview throu
Page 11 and 12: Adrenal insufficiency Clinical vari
Page 13 and 14: AD in medical vocabularies • Syno
Page 15 and 16: MeSH Disease Endocrine system disea
Page 17 and 18: SNOMED International Disease/Diagno
Page 19 and 20: SNOMED CT (UMLS view) Disorder of e
Page 21 and 22: ICD-10 Endocrine, nutritional and m
Page 23 and 24: organize concepts Disease Endocrine
Page 25 and 26: UMLS view Immune system diseases Di
Page 27 and 28: Categorize concepts • High-level
Page 29 and 30: Lexical knowledge Adrenal gland dis
Page 31 and 32: Additional knowledge: UMLS editors
Page 33 and 34: Overview of the UMLS UMLS Knowledge
Page 35 and 36: UMLS Metathesaurus
Page 37 and 38: Source Vocabularies (2011AA) • 16
Page 39 and 40: Biomedical terminologies (cont’d)
Page 41 and 42: Integrating subdomains Clinical rep
Page 43 and 44: Addison’s Disease: Concept Diseas
Page 45 and 46: Metathesaurus Evolution over time
Page 47 and 48: Symbolic relations • Relation ◦
Page 49 and 50: Everything else • Co-occurrence i
Page 51: UMLS Semantic Network
Page 55 and 56: Associative (non-isa) relationships
Page 57 and 58: Relationships may inherit semantics
Page 59 and 60: SPECIALIST Lexicon • Content ◦
Page 61 and 62: Orthography • Spelling variants
Page 63 and 64: Lexical tools • To manage lexical
Page 65 and 66: Normalization Example Hodgkin Disea
Page 67 and 68: Indexes • Word index ◦ word to
Page 69 and 70: Overview of the UMLS Conclusions
Page 71 and 72: Trans-namespace integration Addison
Page 73 and 74: Dr. Kin Wah Fung SNOMED CT integrat
Page 75 and 76: Different views of synonymy • In
Page 77 and 78: UMLS editing process • Analysis o
Page 79 and 80: Examples of split SNOMED CT synonym
Page 81 and 82: SNOMED CT Concept View Acetaminophe
Page 83 and 84: Common types of cross-hierarchy mer
Page 85 and 86: Extent of disagreement • Splittin
Page 87 and 88: Dr. Kin Wah Fung Representing SNOME
Page 89 and 90: SNOMED CT is more complex than othe
Page 91 and 92: Rich Release Format (RRF) • Also
Page 93 and 94: SNOMED CT Concepts SNOMED CT Concep
Page 95 and 96: SNOMED CT Relationships SNOMED CT R
Page 97 and 98: Dr. Olivier Bodenreider Applying UM
Page 99 and 100: Beta-blocker in SNOMED CT
Page 101 and 102: Finding variants in other sources S
Page 103 and 104:
Ms. Janice H. Willis Applying UMLS
Page 105 and 106:
SNOMED CT QA What we learn from mai
Page 107:
UTS browser • https://uts.nlm.nih
Page 110 and 111:
Search options include limiting by
Page 112 and 113:
Metathesaurus Browser Tree search t
Page 114 and 115:
SNOMED CT-centric display Click CUI
Page 116 and 117:
Metathesaurus Browser Tree search d
Page 118:
http://www.nlm.nih.gov/research/uml
Page 121 and 122:
Getting started with the UTS API
Page 123 and 124:
Getting a proxy granting ticket pri
Page 125 and 126:
Getting a single-use proxy ticket p
Page 127 and 128:
Mapping • ‘Mapping’ generally
Page 129 and 130:
Mapping terms to a terminology •
Page 131 and 132:
Example of synonym substitution •
Page 133 and 134:
Inter-terminology mapping • Make
Page 135 and 136:
The IntraMap algorithm (2) 2. A tar
Page 137 and 138:
Use of MetaMap for inter-terminolog
Page 139 and 140:
Motivation • Translation ◦ Seed
Page 141 and 142:
Multi-lingual vocabularies in the U
Page 143 and 144:
Finding terms in other languages
Page 145 and 146:
Addison’s disease in UMLS
Page 147 and 148:
C0001403 in Dutch Source Term type
Page 149 and 150:
Dr. James T. Case Use case: The rol
Page 151:
Access to the USCRS
Page 154 and 155:
Dentistry SNOMED CT International 1
Page 156 and 157:
Why integrate UMLS with a SCT brows
Page 158 and 159:
No SNOMED CT Description Informs yo
Page 160 and 161:
UMLS Concept Concept exists in ICD9
Page 162 and 163:
References: UMLS home page • UMLS
Page 164 and 165:
References • UMLS as a research p
Page 166 and 167:
References • SNOMED CT and UMLS
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Using SNOMED CT with the UMLS - Medical Ontology Research

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