1

Limning the CTS Ontology Landscape

Barry Smithhttp://ontology.buffalo.edu/smith

Basic science (e.g. pharma data)

Hospital #1 data

Hospital#2 data

Lab#1 data

Clinic #1 data

HIPAA

Basic science data

Data Warehouse

Public

Non-public

What exists

translation

2

With thanks to Tom Beale, Ocean Informatics

Enterprise

Comprehensive Basic

Components

EHR

Multimediagenetics

workflow

identity

Clinicalref data Clinical

models

terms

Security / access control

realtimegateway

telemedicine

HILS

otherprovider

UPDATEQUERY

demographics

guidelinesprotocolsInteractions

DSLocal

modelling

notifications

DSS

PAS

billing

portal

Alliedhealthpatient

PAYER

Msg gateway

Imaging lab

ECG etc

Path lab

LAB

Secondaryusers

Online drug,Interactions DB Online

archetypes

Online terminology

Online Demographic

registries

PatientRecord

Basic science (e.g. pharma data)

Hospital #1 data

Hospital#2 data

Lab#1 data

Clinic #1 data

HIPAA

Basic science data

Data Warehouse

Public

Non-public

What every CTS institution would like to have

translation

4

translation

Basic science (e.g. pharma data)

Hospital #1 data

Hospital#2 data

Lab#1 data

Clinic #1 data

HIPAA

Basic science data

Data Warehouse

Public

Non-public

More (and better?) EHR data

translation

5

translation“Meaningful Use”Coding Systems

Basic science (e.g. pharma data)

Hospital #1 data

Hospital#2 data

Lab#1 data

Clinic #1 data

HIPAA

Basic science data

Data Warehouse

Public

Non-public

Strategies to overcome the complexity and incompatibility of coding schemes of EHRs

translation

6

translation“Meaningful Use”Coding Systems

i2b2 (with ontology

cells)

HOM (Health

Ontology Mapper

7

Coding schemes and terminologies

ICD, SNOMED, …– are slow to change– do not interoperate well with structured basic

biology data– are not fully open source– are tied to multiple competing EHR systems– are not optimized for research

And therefore– do not support translation

8

Hospital #1 data

Hospital#2 data

Roswell data

Clinic #1 data

HIPAA

Basic science data

Data Warehouse

Public

Basic science (e.g. pharma data)

Non-public

It is generally recognized that ontologies must play some part in the solution to these problems

i2b2 (with ontology

cells)

HOM (Health

Ontology Mapper

Gene Ontology

Hospital #1 data

Hospital#2 data

Roswell data

Clinic #1 data

HIPAA

Basic science data

Data Warehouse

Public

Basic science (e.g. pharma data)

Non-public

Proposed solution: extend the Gene Ontology with a consistent set of small, agile, open ontology

modules for clinical domains

9

Open Biomedical Ontologies Foundry

RELATION TO TIME

GRANULARITY

CONTINUANT OCCURRENT

INDEPENDENT DEPENDENT

ORGAN ANDORGANISM

Organism(NCBI

Taxonomy)

Anatomical Entity(FMA, CARO)

OrganFunction

(FMP, CPRO) Phenotypic

Quality(PaTO)

Biological Process

(GO)CELL AND CELLULAR

COMPONENT

Cell(CL)

Cellular Compone

nt(FMA, GO)

Cellular Function

(GO)

MOLECULEMolecule

(ChEBI, SO,RnaO, PrO)

Molecular Function(GO)

Molecular Process

(GO)Open Biomedical Ontologies (OBO) Foundry

(First Draft) 10

OBO Foundry approach extended into other domains

11

NIF Standard Neuroscience Information Framework

ISF Ontologies Integrated Semantic Framework

OGMS and Extensions Ontology for General Medical Science

IDO Consortium Infectious Disease Ontology

cROP Common Reference Ontologies for Plants

OGMS and Its Extensions

Ontology of Medically Relevant Social Entities (OMRSE)

Vital Sign Ontology (VSO)

Mental Diseases

Examples of OGMS applied to specific diseases.

Oral Health and Disease ontology

Infectious Disease Ontology (IDO)

http://code.google.com/p/ogms/

12

IDO and Its Extensions

IDO – Brucellosis

IDO – Dengue Fever

IDO – Influenza

IDO – Malaria

IDO – Staphylococcus Aureus Bacteremia

IDO - Vector Surveillance and Management

VO – Vaccine Ontology

13

Hospital #1 data

Hospital#2 data

Roswell data

Clinic #1 data

HIPAA

Data Warehouse

Public

Basic science (e.g. pharma data)

Non-public

Alzheimer’s Disease

Staph Aureus

Bacteremia

Using OGMS as basis, create small ontologies for specific

clinical domains

Sleep Dis-

orders

14

Open Biomedical Ontologies Foundry

Basic science data

15

Hospital #1 data

Hospital#2 data

Roswell data

Clinic #1 data

HIPAA

Basic science data

Data Warehouse

Basic science (e.g. pharma data)

Non-public

Extend this approach to the workings of the CTS institution itself

Clinical Neurology

Cancer Pathology etc.

Resource dataPublications, patents, equipment,

samples, expertise, grants, lab activities, clinical research

activities, clinical trials

Semi-public

Hospital #1 data

Hospital#2 data

Roswell data

Clinic #1 data

HIPAA

Data Warehouse

Basic science (e.g. pharma data)

Non-public

Extend this approach to the workings of the CTS institution itself

Clinical Trial Ontology

Consent Ontology etc.

Resource dataPublications, patents, equipment,

samples, expertise, grants, lab activities, clinical research

activities, clinical trials

Semi-public

16

OBI : Ontology for Biomedical

InvestigationsOGMS

Open Biomedical Ontologies Foundry