The Health Ontology Mapper (HOM) Method Clinical & Translational Science

Ontology Workshop (NCBO/CTSA)

April 24, 2012

Rob Wynden - Chief Scientist,Ketty Mobed PhD – Lead Ontologist,IHPS Informatics Lab, Phillip R Lee Institute of Health Policy Studies,UCSF

Health Ontology Mappera collaborative project

• UCSF

• U Penn

• UCD

• U Rochester

• UW

• UT Houston

• And many others…

Clinical Data Ontologies

• Clinical (hospital) data is messy and not as cleanly defined. On our project we are developing a means of describing all hospital data with ontologies.

• This requires the creation of new ontologies and maps.

• This requires new toolkits (like HOM) as well as extensions to BioPortal. We need to empower the very few ontologists so that large numbers of hospitals can be accessed.

HOM Integration with BioPortal

• The HOM project is promoting a new method for working with Biomedical Ontologies.

• In previous talks we have focused on the computer science needed to service a diverse population of hospitals including community hospitals and not just well funded CTSA sites.

• In this talk we will instead focus on the HOM Method of utilizing BioPortal and how that can improve terminologist efficiency to make deployment in community hospitals easier.

Standard ETL Model(Extract, Transform, Load)

HOM Model(Terminology Server Driven ELT)

HOM BioPortal Content

• HOM Source Ontologies

• HOM Filter Rules

• HOM Bool Mapping Rules

• HOM Variables

• HOM Parent Ontologies (Ontology Inheritance)

• HOM BioPortal Content drives the automated translation of client site instance data without transferring any of that data to BioPortal.

HOM Source Ontologies• Source software schemas can be translated into OWL

ontologies via 2 methods– Schemas can be translated into a simple taxonomy of tables and

columns– Schemas can be translated into NCBO Datamaster

representations that include foreign key relations etc.

• HOM Source Ontologies allow hospitals to describe source instance data in OWL format to facilitate BioPortal driven mapping and analysis.

• HOM does not enforce what target ontologies we map sources to (Map to any ontology).

Example Source Ontology viewed with

Protege

•A Source Ontology for the CMS data generated for the bundled payments intitiative

HOM Filters

• HOM Filters are BioPortal tags (tagged notes) that identify source ontologies objects as PHI.

• HOM Filters allow a wide variety of data to be “scrubbed” during the Extraction process before any data is loaded into the database.

• HOM Filters can describe how NCBO Annotator should be run on the client, how to remove digits from ZIP codes and how MRN’s should be proxied etc…

Example HOM Filters

HOM Filters describe client (hospital site) data translations via BioPortal content

HOM Filter Types

Each input element can have 1 or more filters:

•Types of HOM Filters:– HOM_PHI_ProxyID– HOM_NCBOAnnotator purlzURI– HOM_ZIP_Scrubber digit-count– HOM_DateShifter range– HOM_PhysioNetDeID– HOM_TextSplitter regex– Etc…

HOM Parent Ontologies

• HOM allows terminologists to Inherit mapping content from previous ontology map sets.

• HOM maps can be over-ridden in derived ontologies to allow the efficient sharing of mapping content

Example HOM Parent Ontology Tag

HOM Bool Maps

• HOM Bool Maps allow the complex description of boolean expressions for describing instance maps.

• A HOM Bool that evaluates to ‘true’ results in a data transformation of data into the target ontology.

• HOM Variables are shorthand notations that make it easier to read HOM Bool statements.

Example HOM Bool Statement

The Requirements

- All required data ontologies need to be available

on BioPortal (BP)

- Parent ontologies already identified

- To-be-used Boolean syntax required to be

generated and tested via chart review

-URIs and ontology annotation label to be

identified for source ontologies in BP

A Cookbook Method

• Required Components• .xls Spreadsheet

• with Boolean Statements

• Protégé 3.5 • Mapping Master (plug-in)• (Natasha Noy, Martin O’Connor @ Stanford)

• NCBO BioPortal• REST Services

• HOM

• (Demo)

LIVE DEMO

• Ketty Mobed will provide live demo of HOM Variable and HOM Bool statements here.

TUTORIAL DEMO

HOM Strategy• HOM is a method with an associated open source

(BSD licensed) tool. We are providing a multivariate, many to one mapping method that supports complex biomedical transformations.

• Internally we are focused on tactical initiatives of immediate benefit to medical centers.

• HOM does not enforce what maps are run. It’s a method/tool and not an enforcement agency.

• HOM seeks to create radical efficiency for ontologists such that it is feasible for the existing population of experts to map the data for all 5000 US hospitals.

A New Clinical Role for NCBO BioPortal

• Clinical Ontologies for Hospital Instance Data:

• LOINC & clinical lab reference interval (RI) binning

• New Disease Ontologies

• Hospital financial analysis (ICD-10 Autocoding)

• Clinical Registries (Orthopedics, ICU, Hospitalists)...

• Distributed computing for HIPAA Compliance Protection

• Comparative Effectiveness Research and QI

New Clinical Ontology Content on BioPortal

• Clinical Support requires commercially supported terminologies. Some of those are private out of necessity (commercial maps).

• DRG support (Diagnostic Related Groups)

• CPT

• Demographics

• Co-morbid Conditions, etc., etc,…

(We can map to any ontology on BioPortal.)

Licensed Content

• Ontologies that describe proprietary content must have restricted access that only allows access by institutions with a legal license.

• Medical centers have long had a tendency to support commercially backed standards initiatives. They do this in order to obtain sufficient commercial operational support.

The HOM Workflow (ELT … not ETL)

1. Source/Target Ontologies, HOM filters and HOM maps are described on BioPortal

2. All data de-identified on Extract as a HIPAA Limited Data Set by automatically following BioPortal HOM Filter Rules

3. Bulk import files are generated for the local data and Loaded to a database

4. Local data is then Transformed to targets ontologies for re-use by automatically calculating BioPortal HOM Boolean Map Expressions

1. Curation of Source ontologies (Protégé)

2. Curation of Target ontologies (Protégé)

3. Upload to BioPortal

4. Linking Source to Target (HOM Maps)

HOM and BioPortal REST Services map local data to Targets for distributed aggregates

Current Projects

• ICD-10 detail code mapping

• Orthopedics Outcomes for cost containment.

• ICU Outcomes (ICOM) ontology : automated generation from an EMR

• Creation of a turnkey HOM analytics network Appliance (SemGraphDB)

• Connecting the SemGraphDB Appliance to SHRINE for the SHRINE National Demo

Questions

Thank you!

Rob Wynden

Rob.wynden@ucsf.edu

Chief Scientist, IHPS Informatics Lab

Phillip R Lee Institute of Health Policy Studies

UCSF