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![Page 1: The Health Ontology Mapper (HOM) Method Clinical & Translational Science Ontology Workshop (NCBO/CTSA) April 24, 2012 Rob Wynden - Chief Scientist, Ketty.](https://reader036.fdocuments.in/reader036/viewer/2022062407/56649e005503460f94ae9082/html5/thumbnails/1.jpg)
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
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Health Ontology Mappera collaborative project
• UCSF
• U Penn
• UCD
• U Rochester
• UW
• UT Houston
• And many others…
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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.
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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.
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Standard ETL Model(Extract, Transform, Load)
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HOM Model(Terminology Server Driven ELT)
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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.
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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).
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Example Source Ontology viewed with
Protege
•A Source Ontology for the CMS data generated for the bundled payments intitiative
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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…
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Example HOM Filters
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HOM Filters describe client (hospital site) data translations via BioPortal content
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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…
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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
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Example HOM Parent Ontology Tag
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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.
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Example HOM Bool Statement
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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
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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)
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LIVE DEMO
• Ketty Mobed will provide live demo of HOM Variable and HOM Bool statements here.
TUTORIAL DEMO
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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.
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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
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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.)
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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.
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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
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1. Curation of Source ontologies (Protégé)
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2. Curation of Target ontologies (Protégé)
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3. Upload to BioPortal
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4. Linking Source to Target (HOM Maps)
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HOM and BioPortal REST Services map local data to Targets for distributed aggregates
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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
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Questions
Thank you!
Rob Wynden
Chief Scientist, IHPS Informatics Lab
Phillip R Lee Institute of Health Policy Studies
UCSF