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Developing i2b2 Ontologies for the Long Haul
Lori Phillips, MS
Partners HealthCare Systems, Inc
April 25, 2012
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National Centers for Biomedical Computing
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What is i2b2?
Software for explicitly organizing and transforming person-oriented clinical data in a way that is optimized for research
Allows integration of clinical data, trials data, and genotypic data
A portable and extensible application framework Modular software architecture allows additions without disturbing core
parts Available as open source at https://www.i2b2.org
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Where is it used?CTSA’s Boston University Case Western Reserve University (including Cleveland Clinic) Children's National Medical Center (GWU), Washington D.C. Duke University Emory University (including Morehouse School of Medicine and Georgia Tech ) Harvard University (including Beth Israel Deaconness Medical Center, Brigham and
Women's Hospital, Children's Hospital Boston, Dana Farber Cancer Center, Joslin Diabetes Center, Massachusetts General Hospital)
Medical University of South Carolina Medical College of Wisconsin Oregon Health & Science University Penn State MIlton S. Hershey Medical Center Tufts University University of Alabama at Birmingham University of Arkansas for Medical Sciences University of California Davis University of California, Irvine University of California, Los Angeles* University of California, San Diego* University of California San Francisco University of Chicago University of Cincinnati (including Cinncinati Children's Hospital Medical Center) University of Colorado Denver (including Children's Hospital Colorado) University of Florida University of Kansas Medical Center University of Kentucky Research Foundation University of Massachusetts Medical School, Worcester University of Michigan University of Pennsylvania (including Children's Hospital of Philadelphia) University of Pittsburgh (including their Cancer Institute) University of Rochester School of Medicine and Dentistry University of Texas Health Sciences Center at Houston University of Texas Health Sciences Center at San Antonio University of Texas Medical Branch (Galveston) University of Texas Southwestern Medical Center at Dallas University of Utah University of Washington University of Wisconsin - Madison (including Marshfield Clinic) Virginia Commonwealth University Weill Cornell Medical College
Academic Health Centers (does not include AHCs that are part of a CTSA): Arizona State University City of Hope, Los Angeles Georgia Health Sciences University, Augusta Hartford Hospital, CN HealthShare Montana Massachusetts Veterans Epidemiology Research and Information Center
(MAVERICK), Boston Nemours Phoenix Children's Hospital Regenstrief Institute Thomas Jefferson University University of Connecticut Health Center University of Missouri School of Medicine University of Tennessee Health Sciences Center Wake Forest University Baptist Medical Center
HMOs: Group Health Cooperative Kaiser Permanente
International: Georges Pompidou Hospital, Paris, France Hospital of the Free University of Brussels, Belgium Inserm U936, Rennes, France Institute for Data Technology and Informatics (IDI), NTNU, Norway Institute for Molecular Medicine Finland (FIMM) Karolinska Institute, Sweden Landspitali University Hospital, Reykjavik, Iceland Tokyo Medical and Dental University, Japan University of Bordeau Segalen, France University of Erlangen-Nuremberg, Germany University of Goettingen, Goettingen, Germany University of Leicester and Hospitals, England (Biomed. Res. Informatics Ctr.
for Clin. Sci) University of Pavia, Pavia, Italy University of Seoul, Seoul, Korea
Companies: Johnson and Johnson (TransMART) GE Healthcare Clinical Data Services
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Why use i2b2?
Cohort discovery Enables and simplifies research cohort discovery across an institution’s
large, heterogeneous clinical datasets
Hypothesis generation Enables and simplifies analysis of data to support a hypothesis
Retrospective data analysis Enables the retrospective analysis of data to support/refute claims.
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i2b2 Workbench
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Data Model
FACTS The quantitative or factual data being queried
DIMENSIONS Groups of hierarchies and descriptors that define the facts.
STAR SCHEMA A single fact table surrounded by numerous dimension tables.
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i2b2 Star Schema
observation_fact
PK Patient_NumPK Encounter_NumPK Concept_CDPK Observer_CDPK Start_DatePK Modifier_CDPK Instance_Num
End_Date ValType_CD TVal_Char NVal_Num ValueFlag_CD Observation_Blob
visit_dimension
PK Encounter_Num
Start_Date End_Date Active_Status_CD Location_CD*
patient_dimension
PK Patient_Num
Birth_Date Death_Date Vital_Status_CD Age_Num* Gender_CD* Race_CD* Ethnicity_CD*
concept_dimension
PK Concept_Path
Concept_CD Name_Char
observer_dimension
PK Observer_Path
Observer_CD Name_Char
1
∞
∞
∞∞
∞
∞
1
modifier_dimension
PK Modifier_Path
Modifier_CD Name_Char
∞
∞
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Observation (fact table) Primary Keys
Patient_num Distinct number for every patient
Encounter_num Distinct number for every visit
Concept_cd Distinct code for every concept
Observer_cd Distinct code for every observer
Start_date Date-time observation began
Modifier_cd Code to modify concept_cd
Instance_num Mechanism to group concept modifers
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i2b2 Fact Table
In i2b2, an atomic fact is an observation on a patient.
Examples of facts Diagnoses Procedures Lab data Medications Genetic data
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i2b2 Dimension Tables
Dimension tables contain descriptive information about the facts.
Examples Concept dimension describes the concepts stored in the concept_cd
field. Provider dimension contains information about the observer_cd field Patient dimension contains information about the patient_num field Visit dimension contains information about the encounter_num field Modifier dimension contains information about the modifier_cd field
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How does i2b2 use Ontologies?
By and large, the concepts stored in the fact table come from clinical coding systems or ontologies.
Largely dependent on data available to institution Diagnoses ICD9/ICD10/SNOMED Procedures CPT/ICD9 Medications NDC/RXNORM Lab results LOINC Molecular/genomic data Custom or project specific data
Ontologies are used to organize query terms (and concepts) hierarchically.
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Metadata table
Query terms are stored in a separate metadata table.
There is a one-to-one mapping of terms in the metadata to concepts in the dimension table.
The structure of the metadata table is integral to both the visualization of the query terms (tree) and the query mechanism itself.
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Structure of Metadata Table
METADATA
C_HLEVEL INT NULL C_FULLNAME VARCHAR(900) NULL C_NAME VARCHAR(2000) NULL C_SYNONYM_CD CHAR(1) NULL C_VISUALATTRIBUTES CHAR(3) NULL C_TOTALNUM INT NULL C_BASECODE VARCHAR(450) NULL C_METADATAXML TEXT NULL C_FACTTABLECOLUMN VARCHAR(50) NULL C_TABLENAME VARCHAR(50) NULL C_COLUMNNAME VARCHAR(50) NULL C_COLUMNDATATYPE VARCHAR(50) NULL C_OPERATOR VARCHAR(10) NULL C_DIMCODE VARCHAR(900) NULL C_COMMENT TEXT NULL C_TOOLTIP VARCHAR(900) NULL UPDATE_DATE DATETIME NULL DOWNLOAD_DATE DATETIME NULL IMPORT_DATE DATETIME NULL SOURCESYSTEM_CD VARCHAR(50) NULL VALUETYPE_CD VARCHAR(50) NULL
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i2b2 Metadata Root Level Categories
Terms with c_hlevel = 1
Display name is c_name
Icon (folder or container) is determined by c_visualattributes
Example c_fullname: \Diagnoses\
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Query terms are visualized hierarchically in tree
\Diagnoses\ 1
Respiratory system\ 2
Chronic obstructive diseases\ 3
Emphysema\ 4
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Why are hierarchies so important for i2b2?
Hierarchies form the basis of both the visualization of the terms and the query mechanism itself.
select * from metadata where c_fullname like ‘\Diagnoses\Respiratory system\Chronic obstructive
diseases\Emphysema\%’ and c_hlevel = 5
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Structure of Metadata Table
METADATA
C_HLEVEL INT NULL C_FULLNAME VARCHAR(900) NULL C_NAME VARCHAR(2000) NULL C_SYNONYM_CD CHAR(1) NULL C_VISUALATTRIBUTES CHAR(3) NULL C_TOTALNUM INT NULL C_BASECODE VARCHAR(450) NULL C_METADATAXML TEXT NULL C_FACTTABLECOLUMN VARCHAR(50) NULL C_TABLENAME VARCHAR(50) NULL C_COLUMNNAME VARCHAR(50) NULL C_COLUMNDATATYPE VARCHAR(50) NULL C_OPERATOR VARCHAR(10) NULL C_DIMCODE VARCHAR(900) NULL C_COMMENT TEXT NULL C_TOOLTIP VARCHAR(900) NULL UPDATE_DATE DATETIME NULL DOWNLOAD_DATE DATETIME NULL IMPORT_DATE DATETIME NULL SOURCESYSTEM_CD VARCHAR(50) NULL VALUETYPE_CD VARCHAR(50) NULL
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Hierarchies in queries
select patient_num from observation_fact where concept_cd IN (select concept_cd from concept_dimension where concept_path LIKE '\
Diagnoses\Respiratory system\Chronic obstructive diseases\ Emphysema\%')
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i2b2 Ontologies for the Long Haul
How do I create i2b2 metadata for a known ontology? ICD-10
What happens to my legacy clinical data when I have to move to ICD-10?
Merging ICD-9 with ICD-10
How do I handle genomic metadata?
…. Custom metadata?
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NCBO BioPortal ICD-10
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Building an ICD-10 Ontology with NCBO services
Pull data from NCBO via REST services. Reorganize information into i2b2 Metadata format
bioportal/concepts/46302/all<data> <pageNum>1</pageNum> <numPages>1832</numPages> <pageSize>50</pageSize> <numResultsPage>50</numResultsPage> <numResultsTotal>91590</numResultsTotal> <contents class="org.ncbo.stanford.bean.concept. ClassBeanResultListBean"> <classBeanResultList> <classBean> <id>0-ICD10CM</id> <fullId>http://purl.bioontology.org/
ontology/ICD10CM/0-ICD10CM</fullId> <label>ICD-10-CM TABULAR LIST of
DISEASES and INJURIES</label> <type>class</type> <relations> <entry> <string>ChildCount</string> <int>0</int> </entry> ……
METADATA
C_ HLEVEL I NT NULL C_ FULLNAME VARCHAR(900) NULL C_ NAME VARCHAR(2000) NULL C_ SYNONYM_ CD CHAR(1) NULL C_ VI SUALATTRI BUTES CHAR(3) NULL C_ TOTALNUM I NT NULL C_ BASECODE VARCHAR(450) NULL C_ METADATAXML TEXT NULL C_ FACTTABLECOLUMN VARCHAR(50) NULL C_ TABLENAME VARCHAR(50) NULL C_ COLUMNNAME VARCHAR(50) NULL C_ COLUMNDATATYPE VARCHAR(50) NULL C_ OPERATOR VARCHAR(10) NULL C_ DI MCODE VARCHAR(900) NULL C_ COMMENT TEXT NULL C_ TOOLTI P VARCHAR(900) NULL UPDATE_ DATE DATETIME NULL DOWNLOAD_ DATE DATETIME NULL I MPORT_ DATE DATETIME NULL SOURCESYSTEM_ CD VARCHAR(50) NULL VALUETYPE_ CD VARCHAR(50) NULL
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Primary challenges
i2b2 Metadata depends upon hierarchical information c_fullname, c_tooltip maintain the hierarchy from root to leaves
Diseases of the respiratory system \ Chronic lower respiratory diseases \
Emphysema
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Challenges..
NCBO REST service that enables pull of concepts includes immediate parent/child info only
Hierarchy must be computed
<data> <classBean> <id>J43</id> <label>Emphysema</label> <relations> <entry> <string>SuperClass</string> <list> <classBean>
<id>J40-J47</id> <label>Chronic lower respiratory diseases</label> </classBean> </list> </entry> </relations></classBean>
</data>
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NCBO Extraction workflow
NCBORESTXML
NCBORESTXML
Request to extract ontology
i2b2Metadata
ExtractionWorkflow
ICD-10
ExtractedData
Process
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Extracted ICD-10 terms
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Released deliverableshttps://community.i2b2.org/wiki/display/NCBO
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What about my legacy ICD-9 data?
Ideally we would like an i2b2 ontology that integrates ICD-9 into ICD-10.
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Mapping Tool
Tool to verify/(re)assign ontology mappings.
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Navigating the Mapping Tool Tree
Displays terms mapped from one ontology within hierarchy of another
Mapped terms are displayed adjacent to terms they are mapped to and appear in bold
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Adding a new mapping
ICD9:269.3, Mineral deficiency should appear for ICD10:E63 Other nutritional deficiencies
Copy term ICD9:269.3
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Adding a new mapping
Paste onto ICD10:E63 Other nutritional deficiencies
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Move a mapping
Ascorbic acid deficiency (ICD9:267) can be moved down one level to Ascorbic acid deficiency (ICD10:E54)
Drag and drop down the term one level.
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Unmap a mapping
ICD9:416.8 Other chronic pulmonary heart diseases appears in two places: the one attached to ICD10:I27.2 appears incorrect and can be unmapped.
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The Unmapped Terms List
Free form list of terms to be mapped
Locate term you wish to map to in the hierarchy tree. Drag from table to term in the tree.
If you make a mistake you can either reassign the mapped term within the tree or unmap it from tree.
Unmap will cause it to reappear in the unmapped terms list if the term has no other mappings.
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Assigning an unmapped term
Drag from unmapped terms list
Drop onto term we are mapping to
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Unmapping a term
Drag term from tree
Drop onto unmapped terms list
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Search Unmapped Terms By Name
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Search Unmapped Terms by Code
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Mapped Terms Viewer
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Search Mapped Terms By Code
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Search Mapped Terms By Name
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Merging Ontologies
Mapping tool provides a visualization of what the merged ontologies would look like
What if we could extract a single metadata table from this?
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Integration tool
Request to integrate
IntegrationWorkflow
ICD9 into ICD-10
ICD-10 merged with ICD9 terms
MapperCell
For each mapped ICD-9 terms, compute ICD-10
hierarchy
Mapped ICD-9 terms
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How to handle genomic data
Ability to organize the variants for ease of navigation Needs may differ between geneticist, physician, research scientist
Ability to query for the variant in the workbench Genomic labs may report data differently Define the variant so it may be reliably identified over time Implication is that the identifier for the variant does not change over time
or is maintainable.
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How to (reliably) identify a genomic variant?
All of them??
HGVSName ?
Gene name + flanking sequences ?
Chr location,Nucleotide subst ?
RS# ?
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RS number
Uniquely identifies a variant over time ….but….
Novel variants may not have rs number User may not want to submit to dbSNP
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Gene name + flanking sequences
Not guaranteed if gene has several isoforms EGFR
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HGVS Name
Uniquely identifies variant within a referenced and versioned accession and details the nucleotide substitution.
NM_005228.3:c.2155G>T
RefSeq accession Position
Coding DNA
Nucleotidesubstitution
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Is there a common denominator in all of this?
Yes … all ultimately describe variant location on a chromosome.
Nucleotide substitution defines the physical manifestation of the variant.
WE PROPOSE: HGVS name (n/t subst, positional info) Flanking sequences (a way to verify positional info)
AS A WAY TO UNEQUIVOCALLY EQUATE TWO VARIANTS ACROSS DOMAINS ACROSS VERSIONS
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Structure of Metadata Table
METADATA
C_HLEVEL INT NULL C_FULLNAME VARCHAR(900) NULL C_NAME VARCHAR(2000) NULL C_SYNONYM_CD CHAR(1) NULL C_VISUALATTRIBUTES CHAR(3) NULL C_TOTALNUM INT NULL C_BASECODE VARCHAR(450) NULL C_METADATAXML TEXT NULL C_FACTTABLECOLUMN VARCHAR(50) NULL C_TABLENAME VARCHAR(50) NULL C_COLUMNNAME VARCHAR(50) NULL C_COLUMNDATATYPE VARCHAR(50) NULL C_OPERATOR VARCHAR(10) NULL C_DIMCODE VARCHAR(900) NULL C_COMMENT TEXT NULL C_TOOLTIP VARCHAR(900) NULL UPDATE_DATE DATETIME NULL DOWNLOAD_DATE DATETIME NULL IMPORT_DATE DATETIME NULL SOURCESYSTEM_CD VARCHAR(50) NULL VALUETYPE_CD VARCHAR(50) NULL
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Genomic MetadataXML record
GenomicMetadata Version 1.0 ReferenceGenomeVersion hg18 SequenceVariant HGVSName NM_0005228.3:c.2155G>T SystematicName c.2155G>T SystematicNameProtein p.Glu719Cys AaChange missense DnaChange substitution SequenceVariantLocation GeneName EGFR FlankingSeq_5 GAATTCAAAAAGATCAAAGTGCTG FlankingSeq_3 GCTCCGGTGCGTTCGGCACGGTGT RegionType exon RegionName Exon 18 Accessions Accession Name NM_005228 Type mrna (NCBI) Accession Name NP_005219 Type protein (NCBI) Accession Name NT_004487 Type contig (NCBI) ChromosomeLocation Chromosome chr7 Region 7p12 Orientation +
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Organizational challenges
By Disease?
By Gene?
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Combining equivalent terms
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How to handle custom (local) metadata
Edit Tool ideal for creating small, non-standard ontology for a local project.
Consider the case for classifying patients as smokers, non-smokers or smoking status unknown
The Custom Metadata folder is designed for use with the creation of local terms.
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Create a “Smoking status” folder
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Populate folder with “Smoker”, “Non-smoker”, etc
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Smoking status custom metadata
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www.i2b2.org
https://community.i2b2.org/wiki
http://bioportal.bioontology.org