Clinical Terminologies from the Research Perspective - National Institutes of Health · 2017. 3....
Transcript of Clinical Terminologies from the Research Perspective - National Institutes of Health · 2017. 3....
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Clinical Terminologiesfrom the Research Perspective
March 13, 2017
Olivier Bodenreider
Lister Hill National Centerfor Biomedical CommunicationsBethesda, Maryland - USA
Health Facts Boot Camp
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Learning objectives
Describe the history of biomedical ontologiesList and describe the main biomedical ontologies
used in 21st century healthcareDiscuss the purpose of biomedical ontologies in
knowledge management, clinical decision support and analytics
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References Review articles
Bodenreider O, Stevens R. Bio-ontologies: current trends and future directions. Brief Bioinform. 2006 Sep;7(3):256-74.
Cimino JJ, Zhu X. The practical impact of ontologies on biomedical informatics. Yearb Med Inform. 2006:124-35.
Bodenreider O. Biomedical ontologies in action: role in knowledge management, data integration and decision support. Yearb Med Inform. 2008:67-79.
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Additional references
Cimino JJ. Desiderata for controlled medical vocabularies in the twenty-first century. Methods Inf Med. 1998 Nov;37(4-5):394-403.
Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D267-70.
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MedicalOntologyResearch
Contact:Web:
[email protected]://mor.nlm.nih.gov
Olivier Bodenreider
Lister Hill National Centerfor Biomedical CommunicationsBethesda, Maryland - USA
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Outline
(Brief) Historical perspective
“High-Impact” Biomedical Ontologies Structural perspective
Biomedical Ontologies “in Action” Functional perspective
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Historical perspective
Clinical TerminologiesPart 1
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History of Medical Ontologies
1603 1975
ICD9
1900
ICD
18551785
SynopsisNosologiaeMethodicae
OPCS
SNOPCPT
MESHICD
MeSH
OPCSEmTree
SNOPCPT
1700
OPCS3
CTV3READ
1975 20051985 1995
ICPCSNOMED-2
SNOMEDInternational SNOMED-RT
SNOMED-CT
GALEN DM&DUMLS
FMA
OPCS4 OPCS4.3
(courtesy of J. Rogers)[Bodenreider, BIB 2006]
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“High-Impact” Biomedical Ontologies
A Structural Perspective
Clinical TerminologiesPart 2
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Overview
Structural perspective What are they (vs. what are they for)?
“High-impact” biomedical ontologies International Classification of Diseases (ICD) Logical Observation Identifiers, Names and Codes (LOINC) SNOMED Clinical Terms Foundational Model of Anatomy Gene Ontology RxNorm Medical Subject Headings (MeSH) NCI Thesaurus Unified Medical Language System (UMLS)
[J. Cimino, YBMI 2006]
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International Classification of Diseases
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ICD Characteristics (1)
Current version: ICD-10 (2016) Annual updates
Type: ClassificationDomain: DisordersDeveloper: World Health Organization (WHO) Funding: WHO Publicly available: YesUsed for: Mortality and morbidity statistics
worldwideURL: http://www.who.int/classifications/icd/en/
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ICD Characteristics (2)
Number of Concepts: 12,320 (ICD-10, 2004) Terms: 1 per concept (tabular)
Major organizing principles: Tree (single inheritance hierarchy) No explicit classification criteria
Idiosyncratic inclusion/exclusion mechanism .8 slots for Not elsewhere classified (NEC) .9 slots for Not otherwise specified (NOS)
Specific coding rules Distribution: Proprietary format
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ICD Top levelhttp://apps.who.int/classifications/icd10/browse/
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ICD Example
Idiosyncratic inclusion/exclusion criteria
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ICD Example
Not elsewhere classified (NEC)Not otherwise specified (NOS)
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ICD-10-CM
Derived from: ICD-10 Finer-grained (both clinically and administratively)
Type: Classification 92,042 codes (2015) Terms: 1.2 per concept
Domain: Disorders Developer: National Center for Health Statistics
(NCVHS) Funding: U.S. Government Publicly available: Yes Used for: Billing URL: http://www.cdc.gov/nchs/icd/icd10cm.htm
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ICD-10 vs. ICD-10-CM
x6
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ICD-10 vs. ICD-10-CM
x24
W58.01A Bitten by alligator, initial encounterW58.01D Bitten by alligator, subsequent encounterW58.01S Bitten by alligator, sequela
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Logical Observation Identifiers, Names and Codes (LOINC)
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LOINC Characteristics (1)
Current version: 2.59 (Feb. 2017) 2 annual releases
Type: Controlled terminology*Domain: Laboratory and clinical observationsDeveloper: Regenstrief Institute Funding: NLM and other sources Publicly available: YesUsed for: information exchange URL: https://loinc.org/
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LOINC Characteristics (2)
Number of Concepts: 73,958 active codes (2.52, June 2015) Terms: 1 per concept (“long name”)
Major organizing principles: No hierarchical structure among the main codes 6 axes
Component (analyte [+ challenge] [+ adjustments]) Property Timing System Scale [Method]
Distribution: proprietary database format
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LOINC Example
Sodium [Moles/volume] in Serum or Plasma[the molar concentration of sodium is measured in the plasma (or serum), with quantitative result]
Axis ValueComponent Sodium
Property SCnc – Substance Concentration (per volume)
Timing Pt – Point in time (Random)
System Ser/Plas – Serum or Plasma
Scale Qn – Quantitative
Method --
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https://search.loinc.org/
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SNOMED Clinical Terms
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SNOMED CT Characteristics (1)
Current version: January 31, 2017 2 annual releases
Type: Reference terminology / ontology Domain: Clinical medicine Developer: IHTSDO Funding: IHTSDO member countries Publicly available: Yes* Used for: clinical documentation, information
exchange, analytics URL: http://www.ihtsdo.org/
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SNOMED CT Characteristics (2)
Number of Concepts: 320,912 active concepts (Sept. 2016) Terms: 2.6 per concept (“descriptions”)
Major organizing principles: Polyhierarchy Rich set of associative relationships Logical definitions (incomplete: many primitives) Built using description logics (EL++)
Distribution: RF2 (proprietary)
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SNOMED CT Top levelhttp://browser.ihtsdotools.org/
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SNOMED CT Example
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SNOMED CT Example
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RxNorm
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RxNorm Characteristics (1)
Current version: March 2017 Monthly releases (+weekly updates)
Type: Controlled terminologyDomain: Drug namesDeveloper: NLM Funding: NLM Publicly available: Yes*Used for: e-prescribing, information exchange,
analyticsURL: http://www.nlm.nih.gov/research/umls/rxnorm/
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RxNorm Characteristics (2)
Number of Concepts: 117,774 (March 2016) Terms: 1.5 per concept
Major organizing principles: Generic vs. brand Ingredient + Strength + Dose form No hierarchical structure; rich graph of associative
relations Integrates all major US drug information sources No clinical information
Distribution: similar to UMLS RRF format
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RxNorm Normalized form
Ingredient
Dose form
Strength
Ingredient
IngredientStrength Dose form
Strength
4mg/ml
Ingredient
Fluoxetine
Dose form
Oral Solution
Semantic clinical drug component
Semantic clinical drug
Semantic clinical drug form
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RxNorm ExampleIngredient
C. Drug Comp. C. Drug Form
C. Drug
Brand Name
B. Drug Comp. B. Drug Form
B. Drug
B. PackG. Pack
Zithromax 250 MG Oral TabletAzithromycin 250 MG Oral Tablet
Z-PAK{6 (Azithromycin 250 MG Oral Tablet) } Pack
Azithromycin 250 MG Azithromycin Oral Tablet Azithromycin 250 MG[Zithromax]
Azithromycin Oral Tablet[Zithromax]
ZithromaxAzithromycin
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RxNorm Relations among drug entities
https://mor.nlm.nih.gov/RxNav/
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Product Identification: NDCs
National Drug Codes Product identification system Three components
Manufacturer Product Packaging
Introduced in 1972 by FDAOnly format permitted by NCPDPMandated by HIPAA regulations for drug
transactionsSource: Dan Malone
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NDC Elements: 3 segments
XXXXX-XXXX-XX
Manufacturer Product Packaging
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NDC Forms
XXXX-XXXX-XX (4-4-2)
XXXXX-XXX-XX (5-3-2)
XXXXX-XXXX-X (5-4-1)
0555-0831-02 (Teva Pharmaceuticals USA, Inc.; 100 in 1 BOTTLE)
Warfarin Sodium 1 MG Oral Tablet
21695-672-30 (Rebel Distributors Corp; 30 in 1 BOTTLE)
50090-1213-0 (A-S Medication Solutions; 30 in 1 BOTTLE)
→ 0XXXX XXXX XX
→ XXXXX 0XXX XX
→ XXXXX XXXX 0X
→ 00555083102
→ 21695067230
→ 50090121300
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Biomedical Ontologies “in Action”
A Functional Perspective
Clinical TerminologiesPart 3
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Overview
Functional perspective What are they for (vs. what are they)?
“High-impact” biomedical ontologies 3 major categories of use
Knowledge management (indexing and retrieval of data and information, access to information, mapping among ontologies)
Data integration, exchange and semantic interoperability Decision support and analytics (data selection and
aggregation, decision support, natural language processing applications, knowledge discovery)
[Bodenreider, YBMI 2008]
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Knowledge management
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Knowledge management
Annotating data and resources
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Terminology in ontology
Ontology as a source of vocabulary List of names for the entities in the ontology
(ontology vs. terminology)Most ontologies have some sort of terminological
componentNot all surface forms represented
Often insufficient for NLP applications Large variation in number of terms per concept across
ontologies
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Annotating data
Gene Ontology Functional annotation of gene products
in several dozen model organismsVarious communities use the same controlled
vocabulariesEnabling comparisons across model organismsAnnotations
Assigned manually by curators Inferred automatically (e.g., from sequence similarity)
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GO Annotations across species
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Indexing the biomedical literature
MeSH Used for indexing and retrieval
of the biomedical literature (MEDLINE) Indexing
Performed manually by human indexers With help of semi-automatic systems (suggestions)
e.g., Indexing Initiative at NLM
Specific indexing rules
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MeSH MEDLINE indexing
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MeSH MEDLINE indexing
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SNOMED CT/ICD Coding clinical data
SNOMED CT Used for clinical documentation E.g., problem lists
ICD-10-CM Used for coding clinical data for billing purposes Other uses of ICD
Morbidity and mortality reporting worldwide
Specific coding rules
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Knowledge management
Accessing biomedical information
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Resources for biomedical search engines
SynonymsHierarchical relationsHigh-level categorization [Co-occurrence information]Translation
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MeSH “synonyms” MEDLINE retrieval
MeSH entry terms Used as equivalent terms for retrieval purposes
(query expansion) Not always synonymous
Increase recall without hurting precision
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MeSH “synonyms” MEDLINE retrieval
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MeSH hierarchies MEDLINE retrieval
MeSH “explosion” Search for a given MeSH term and all its descendants A search on Adrenal insufficiency also retrieves articles
indexed with its descendant, Addison disease
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Knowledge management
Mapping across biomedical ontologies
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Terminology integration systems
Terminology integration systems (UMLS, RxNorm) help bridge across vocabularies
Uses Information integration Ontology alignment Medication reconciliation
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Integrating subdomains
Biomedicalliterature
MeSH
Genomeannotations
GOModelorganisms
NCBITaxonomy
Geneticknowledge bases
OMIM
Clinicalrepositories
SNOMED CTOthersubdomains
…
Anatomy
FMA
UMLS
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Integrating subdomains
Biomedicalliterature
Genomeannotations
Modelorganisms
Geneticknowledge bases
Clinicalrepositories
Othersubdomains
Anatomy
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Trans-namespace integration
Genomeannotations
GOModelorganisms
NCBITaxonomy
Geneticknowledge bases
OMIMOther
subdomains
…
Anatomy
FMA
UMLSAddison Disease (D000224)
Addison's disease (363732003)
Biomedicalliterature
MeSH
Clinicalrepositories
SNOMED CT
UMLSC0001403
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UMLS Source Vocabularies
154 families of source vocabularies Not counting translations
25 languagesBroad coverage of biomedicine
10.3M names (normalized) 3.4M concepts ~13M relations among concepts
Common presentation
(2016AB)
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Metathesaurus Basic organization
Concepts Synonymous terms are clustered into a concept Properties are attached to concepts, e.g.,
Unique identifier Definition
Relations Concepts are related to other concepts Properties are attached to relations, e.g.,
Type of relationship Source
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Decision support and analytics
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Data selection
The structure of biomedical ontologies helps define groups of values from a high-level value Vs. enumerating all possible values
Useful for data selection in clinical studies ICD is used pervasively for this purpose
E.g., Study on supraventricular tachycardia (SVT), based on 2 high-level ICD codes
Similarity with the definition of value sets for use in the information model
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Data aggregation
Ontologies help partition/aggregate data in data analysis Clinical studies: Study a variable in groups of patients
corresponding to the top level categories in ICD Biology studies: Functional characterization of gene
expression signatures with high-level concepts from the Gene Ontology Recent trend: co-clustering
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Decision support
Clinical decision support Ontologies help normalize the vocabulary and increase
the recall of rules Ontologies provide some domain knowledge and make
it possible to create high-level rules (e.g., for a class of drugs rather than for each drug in the class)
Other forms of decision support Based on automatic reasoning services for OWL
ontologies (e.g., grading gliomas with NCIt)
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Natural language processing applications
Ontologies provide background domain knowledge for NLP applications Question answering Document summarization Literature-based discovery
The UMLS is often used, but other specific resources have been developed
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Knowledge discovery
By standardizing the vocabulary in a given domain, ontologies are enabling resources for knowledge discovery through data mining
Less frequently, the structure of the ontology is leveraged by data mining algorithms
Example of available datasets ICD-coded clinical data (in conjunction with non-
clinical information, e.g., environmental data) Annotation of gene products to the GO (function
prediction)
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Summary
Controlled Vocabularies
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Summary
History of biomedical ontologies Structure of the main clinical ontologies used
ICD, SNOMED CT, LOINC, RxNorm Purpose of biomedical ontologies
Knowledge management, [health information exchange and semantic interoperability], and clinical decision support and analytics
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Topics not discussed
Semantic Web, URIs, Linked Data Ontology creation, Protege Accessing terminology resources (APIs) Ontology repositories
[UMLS], NCBO BioPortal, EBI Ontology Lookup Service NLP, named entity recognition, MetaMap Mapping local terms to standard terminologies VSAC, value sets, common data elements OBO ontologies, OBO Foundry Coordinated development of ontologies, harmonization Boundary between terminology and information model […]
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MedicalOntologyResearch
Contact:Web:
[email protected]://mor.nlm.nih.gov
Olivier Bodenreider
Lister Hill National Centerfor Biomedical CommunicationsBethesda, Maryland - USA