developing i2b2 ontologies for the long haul lori phillips, ms partners healthcare systems, inc...

Developing i2b2 Ontologies for the Long Haul

Lori Phillips, MS

Partners HealthCare Systems, Inc

April 25, 2012

National Centers for Biomedical Computing

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

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

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.

i2b2 Workbench

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.

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

∞

∞

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

i2b2 Fact Table

In i2b2, an atomic fact is an observation on a patient.

Examples of facts Diagnoses Procedures Lab data Medications Genetic data

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

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.

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.

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

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\

Query terms are visualized hierarchically in tree

\Diagnoses\ 1

Respiratory system\ 2

Chronic obstructive diseases\ 3

Emphysema\ 4

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


METADATA


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\%')

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?

NCBO BioPortal ICD-10

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

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

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>

NCBO Extraction workflow

NCBORESTXML

NCBORESTXML

Request to extract ontology

i2b2Metadata

ExtractionWorkflow

ICD-10

ExtractedData

Process

Extracted ICD-10 terms

Released deliverableshttps://community.i2b2.org/wiki/display/NCBO

What about my legacy ICD-9 data?

Ideally we would like an i2b2 ontology that integrates ICD-9 into ICD-10.

Mapping Tool

Tool to verify/(re)assign ontology mappings.

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

Adding a new mapping

ICD9:269.3, Mineral deficiency should appear for ICD10:E63 Other nutritional deficiencies

Copy term ICD9:269.3

Adding a new mapping

Paste onto ICD10:E63 Other nutritional deficiencies

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.

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.

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.

Assigning an unmapped term

Drag from unmapped terms list

Drop onto term we are mapping to

Unmapping a term

Drag term from tree

Drop onto unmapped terms list

Search Unmapped Terms By Name

Search Unmapped Terms by Code

Mapped Terms Viewer

Search Mapped Terms By Code

Search Mapped Terms By Name

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?

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

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.

How to (reliably) identify a genomic variant?

All of them??

HGVSName ?

Gene name + flanking sequences ?

Chr location,Nucleotide subst ?

RS# ?

RS number

Uniquely identifies a variant over time ….but….

Novel variants may not have rs number User may not want to submit to dbSNP

Gene name + flanking sequences

Not guaranteed if gene has several isoforms EGFR

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

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


METADATA


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 +

Organizational challenges

By Disease?

By Gene?

Combining equivalent terms

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.

Create a “Smoking status” folder

Populate folder with “Smoker”, “Non-smoker”, etc

Smoking status custom metadata

www.i2b2.org

https://community.i2b2.org/wiki

http://bioportal.bioontology.org

developing i2b2 ontologies for the long haul lori phillips, ms partners healthcare systems, inc...

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