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?.
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Developing i2b2 Ontologies for the Long Haul
Lori Phillips, MS
Partners HealthCare Systems, Inc
April 25, 2012
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
Massachusetts Veterans Epidemiology Research and Information Center (MAVERICK), Boston
Phoenix Children's Hospital
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
Group Health Cooperative
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
Johnson and Johnson (TransMART)
GE Healthcare Clinical Data Services
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
Respiratory system\ 2
Chronic obstructive diseases\ 3
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
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\%')
<contents class="org.ncbo.stanford.bean.concept. ClassBeanResultListBean">
<label>ICD-10-CM TABULAR LIST of DISEASES and INJURIES</label>
Diseases of the respiratory system \
Chronic lower respiratory diseases \
Request to extract ontology
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.
Request to integrate
ICD9 into ICD-10
For each mapped ICD-9
terms, compute ICD-10
with ICD9 terms
Mapped ICD-9 terms
Nucleotide subst ?
Gene name +
All of them??
Uniquely identifies a variant over time ….but….
Novel variants may not have rs number
User may not want to submit to dbSNP
Not guaranteed if gene has several isoforms
Uniquely identifies variant within a referenced and versioned accession and details the nucleotide substitution.
RefSeq accession Position
Yes … all ultimately describe variant location on a chromosome.
Nucleotide substitution defines the physical manifestation of the variant.
HGVS name (n/t subst, positional info)
Flanking sequences (a way to verify positional info)
AS A WAY TO UNEQUIVOCALLY EQUATE TWO VARIANTS
RegionName Exon 18
Type mrna (NCBI)
Type protein (NCBI)
Type contig (NCBI)