i2b2 national center for biomedical computing i2b2 clinical research chart and hive architecture...

i2b2 National Center for Biomedical Computingi2b2 National Center for Biomedical Computing

i2b2i2b2Clinical Research ChartClinical Research Chartand Hive Architecture and Hive Architecture

Henry ChuehHenry ChuehShawn MurphyShawn Murphy

Isaac Kohane, PIIsaac Kohane, PI


SummarySummary

• Background

• Intro to the Clinical Research Chart (CRC)

• Hive / Cell Software Architecture

• More details on establishing and using the CRC


BackgroundBackground

• Clinical documentation is…clinical

• Lack of systematic approach for organizing clinical data for research

• Ownership issues are unique

• Consent issues are a challenge


Driving Biological ProjectsDriving Biological Projects

• Asthma

• Hypertension

• Huntington’s Disease

• Diabetes


Clinical Research Chart (CRC)Clinical Research Chart (CRC)

• Organize and transform clinical data to maximize its utility for research

• Develop an Application and Database framework to serve this goal

• Establish an architecture that allows data from different studies done on this platform to be integrated


Design of Clinical Research ChartDesign of Clinical Research Chart

Ontology Consent/Tracking Application Pool Management

Services:

Data flowing

Custom Interfaces

Soap/Http interfaces

A program

CRC DB

HL7 MSH|^/&|736401….. PID|102|3231285.….

Text filesText files

XML <Patient1> <image>.….

database

clinicaltrials


Design of Clinical Research ChartDesign of Clinical Research Chart


Services:

Data flowing

Custom Interfaces


A program

Data pipeline/workflow application Pheno/Genotype Database

Visualization and Analysis of database contents

CRC DB Text files

Text files

XML <Patient1> <image>.….

database

clinicaltrials

HL7 MSH|^/&|736401….. PID|102|3231285.….


i2b2 Skeletal Data Flowi2b2 Skeletal Data Flow

Shared data

Shared data

Study specific

data

Study specific

data

Clinical Clinical Research Research

ChartChart

Clinical Clinical Research Research

ChartChart

Enterprise SystemsEnterprise SystemsRegistration, ADT, Labs,Registration, ADT, Labs,

Reports, Clinical Notes, etcReports, Clinical Notes, etc

Enterprise data

source(RPDR)

Enterprise data

source(RPDR)

AnnotationUI

EDCapplications

Local SystemsLocal SystemsSystems not gathered intoSystems not gathered into

Enterprise data warehousesEnterprise data warehouses

i2b2 ETLworkflow

AnnotationService

EDCService

Analyticworkflow


Overall ThemesOverall Themes

• Framework to allow development of application services in a maximally decoupled fashion.

• Linux and Windows OS support• Java and C++ programming languages• Use Cases for construction of CRC come

from Driving Biology Projects and experience with clients of Partners Research Patient Data Registry


Focus on WorkflowFocus on Workflow

• Necessary for both pre-CRC and post-CRC processes

• Needed for scientific flexibility

• Implies a consistent environment for data pipelining and flow control


i2b2 Hivei2b2 Hive

• Formed as a collection of interoperable Cells, or services

• Loosely coupled

• Makes no assumptions about proximity

• Connected by Web services

• Activated by a workflow engine that forms basis of choreography among Cells for complex interactions


Complex choreographyComplex choreography


i2b2 Celli2b2 Cell

• Behaves as a functional service

• Separates interactions conceptually into transactions and semantics

• Focuses on facilitating transactions with simple semantics (e.g., datatype)

• Leaves deep semantics to be defined by the services provided by a Cell

• Does not restrict language implementation


Target layer for i2b2 Target layer for i2b2

TCP/IP

Web Services

I2b2 platform

Semantic Objects


Cell examplesCell examples

• Concept extraction from clinical narratives

• Simple transformations; e.g., basic text format conversion

• Complex encoding; e.g., encoding MIAME in MAGE

• Microarray data normalization

• …


Exposing CellsExposing Cells

• Protocols layered on top of SOAP

• At the WSDL level for integrators; ie, bioinformaticians & software engineers

• At a functional level for investigators

• i2b2 toolkits to allow integrators to expose controlled functionality to investigators (Automator)


Automator ApproachAutomator Approach

investigators

informaticians

Extend Kepler workflow engine

i2b2 Automator


Bird’s eye viewBird’s eye view

Workflow engine

Investigator Portal

CRCRepository


Current ImplementationCurrent Implementation

• Extending Kepler workflow engine for i2b2

• Data model for CRC repository

• Defining protocols necessary for interaction (in addition to SOAP)

• Created Cell for concept extraction from narratives

• Early designs for Automator toolkit


i2b2 Architecture Key Points i2b2 Architecture Key Points

• Leverage existing workflow standards and software

• Use Web services as basic form of interaction

• Assume unlimited choreography, but…

• Provide tools to distill complexity into basic automation for clinical investigators


SW Licensing and DistributionSW Licensing and Distribution

• Commit to Open Source software

• Use GNU Lesser General Public License

• Establish local i2b2 repository exposed through i2b2 website

• Contribute to a more global NCBC SourceForge style repository if it emerges ?NIH Forge

• Keep i2b2 protocols fully open


Interoperability across NCBCInteroperability across NCBC

• Strongly consider Web services as basic protocol for generic shared interactions

• Consider sharing datasets

• Promote diversity of approach and use of shared software (don’t impose uniformity)

• Facilitate/promote NCBC Open Source project teams


Pre-CRC Data Pre-CRC Data Pipeline/WorkflowPipeline/Workflow

Populating the Clinical Research Populating the Clinical Research Chart (CRC)Chart (CRC)


Pre-CRC Data Pipeline/WorkflowPre-CRC Data Pipeline/Workflow

• Use workflow framework to choreograph applications services in specific sequences

• Used to extract, transform, conform, and load data and metadata into the CRC


Pre-CRC Data Pipeline/WorkflowPre-CRC Data Pipeline/Workflow


Services:

Data flowing

Custom Interfaces


OutputInput

A program

increasingly useful

Local or through SOAP service


Ontology ServiceOntology Service


• Manages mappings of terms to common vocabularies• Provides lists of acceptable (enumerated) values for

various attribute and value slots.• Allows for management of hierarchies, groupings, and

relationships between terms

Ontology


Person Consent/Tracking ServicePerson Consent/Tracking Service


• Provides mappings between patient/subject identifiers• Tracks patient/subject consent information• Allows identification of the patient/subject based upon

fuzzy demographic matches

Consent/Tracking


Application Pool (CVS) ServiceApplication Pool (CVS) Service


• Stores programs/scripts used in pipeline• Provides applications to be downloaded when needed• Manages versioning of software• Provides documentation

Application Pool


Management ServiceManagement Service


• Stores workflow execution plan• Starts and controls workflow execution• Schedules workflow execution• Monitors workflow execution and data locations• Controls permissions associated with workflow

execution

Management


Data Pipeline/Workflow ApplicationData Pipeline/Workflow ApplicationUse Case for Asthma DataUse Case for Asthma Data


Services:

Data flowing

Custom Interfaces


OutputInput

A program

RPDR

CRC DB

AsthmaMartData retrieval

Data de-identification

Language processing

Vocabulary matching

Load Data into Mart


Data Pipeline/WorkflowData Pipeline/WorkflowImplementationImplementation

• Define standard XML representation for workflow - MoML

• Define standards for SOAP services and resource discovery

• Adopt and extend open source workflow package (Kepler)

• Prototypes by July timeframe

• BIRN -> NAMIC and LONI collaboration

• Can follow construction details at http://diagon/i2b2

http://diagon/i2b2


Phenotype/Genotype Phenotype/Genotype DatabaseDatabase


Phenotype/Genotype DatabasePhenotype/Genotype DatabasePrinciplesPrinciples

• Analytical database schema that does not need to change with new data types and concepts

• Defined fundamental unit of data (atomic fact) = observation

• Defined metadata strategy

• Various levels of de-identification (reviewed and approved by IRB)


Phenotype/Genotype DatabasePhenotype/Genotype DatabaseArchitectureArchitecture

observation_fact

PK,FK2 Encounter_Id_ePK,FK1,FK2 Patient_Id_ePK Concept_CdPK Provider_IdPK Start_DatePK ValType_CdPK TVal_CharPK NVal_Num

ValueFlag_Cd Quantity_Num Units_Cd End_Date Confidence_Num Observation_Blob Update_Date Download_Date Import_Date Sourcesystem

visit_dimension

PK Encounter_Id_ePK Patient_Id_e

InOutpt_Cd Location_Cd Start_Date End_Date Visit_Blob Update_Date Download_Date Import_Date Sourcesystem_Cd

patient_dimension

PK Patient_Id_e

Vital_Status_Cd Birth_Date Death_Date Sex_Cd Age_In_Years_Num Language_Cd Race_Cd Marital_Status_Cd Religion_Cd Zip_Cd StateCityZip_Path Patient_Blob Update_Date Download_Date Import_Date Sourcesystem_Cd

concept_dimension

PK Concept_Path

Concept_Cd Name_Char Concept_Blob Update_Date Download_Date Import_Date Sourcesystem_Cd

provider_dimension

PK Provider_Path

Provider_Id Name_Char Provider_Blob Update_Date Download_Date Import_Date Sourcesystem(see preprint)


Phenotype/Genotype DatabasePhenotype/Genotype DatabaseUse CaseUse Case

• Smoking observations represented in database

Patient_id_e Concept_cd Start_date Provider_id Confidence_num

Z234 CT-A-SMK 1/1/1997 M0022303 3

Z234 CT-A-SMK 1/1/1998 M0034125 9

Z234 IC9-3051 1/1/2001 M0022303 3

Z234 CT-A-NSK 1/1/2002 M0034125 9

Patient_id_e Birth_date Sex_cd Race_cd Death_date

Z234 3/4/1924 Female Black 4/5/2003

Provider_id Provider_path Name_char

M0022303 MGH\Neurology\M0022303 M0022303

Concept_cd Concept_path Name_char

CT-A-SMK AsthV1\DRptNLP\Tobacco Use\Smoker Smoking

IC9-3051 V2\Diagnosis\Mental Disorders (290-319)\Non-psychotic disorders (300-316)\(305) Nondependent abuse of drugs\(305-1) Tobacco use disorder\(305-11) Tobacco use disorder, co~

Tobacco Use Disorder, continuous use

CT-A-NSK AsthV1\DRptNLP\Tobacco Use\Non smoker Never smoked


Phenotype/Genotype DatabasePhenotype/Genotype DatabaseImplementationImplementation

• Asthma CRC DB “primed” with data from 90,000 patients from Research Patient Data Registry

• Serves as fundamental data structure for i2b2 supported data Querying and Visualization Application Suite

• CRC DB’s able to fuse seamlessly together• Various levels of de-identification to be

supported for data sharing and publication


Visualization and Analysis Visualization and Analysis of CRC databaseof CRC database

Post-CRC workflowPost-CRC workflow


Visualization and AnalysisVisualization and AnalysisPrinciplesPrinciples

• Supported application suite to query and view CRC database contents

• Outside applications for analysis and viewing able to plug in to application suite

• Pipeline/Workflow framework may be used for analysis and re-entry of derived data into CRC database


Visualization and AnalysisVisualization and AnalysisArchitectureArchitecture

• Supported Applications, Querying and Visualization– Standard querying

– Data exploration



• Supported Applications, ontology management– Ontology Management

• Integrate (outside?) population analysis applications

You have picked “seizure disorder”

File Edit

i2b2 ontology management

provenancemapping transform explain

Total20042005

10,1245,0665,058

mappingCounts


File Edit


File Edit



Total20042005

10,1245,0665,058

mappingCounts

Total20042005

10,1245,0665,058

mappingCounts



• Supported applications have plug-in architecture for outside analytic tools:– Standard web-link support with GET and

POST oriented data transfer– Support transfer of specifically transformed

data to outside applications– Complex analysis supported with workflow

application


Visualization and AnalysisVisualization and AnalysisArchitecture - QueryArchitecture - Query


Visualization and AnalysisVisualization and AnalysisArchitecture - ExplorationArchitecture - Exploration


Visualization and AnalysisVisualization and AnalysisArchitecture – Ontology mgmtArchitecture – Ontology mgmt


File Edit



Total20042005

10,1245,0665,058

mappingCounts


File Edit


File Edit



Total20042005

10,1245,0665,058

mappingCounts

Total20042005

10,1245,0665,058

mappingCounts


Visualization and AnalysisVisualization and AnalysisUse CaseUse Case


Visualization and AnalysisVisualization and AnalysisImplementation of analysis toolsImplementation of analysis tools

• Workflow framework to accommodate external analytic applications

CRC DBProgID CA2.3

SN8745

PA5683

SN8745

SN8745

SNOMED CODE

patient id 0000004

account # 347

subject id 4

subject id 4

ProgID CX2.3

ProgID PN5.1 ProgID TH3.0

ProgID SN5.4

ProgID AA3.3

ProgID CN2.3 ProgID XN0.9


Final AssemblyFinal Assembly

statisticsapplication

server

statisticsapplication

server

Gene expression in APOE 4 Allele

Alzheimer's

Seizures

ER visits

Clinic visits

Outcomes calculated every weekSurgeryER visit

microarray (encrypted)

ownershipmanager

encryption

Trauma

Gene-Chips

populationregistry

databasedatabase

microarray (encrypted)

Trauma

Surgery

Multiple sclerosis

Trauma

CT ScanHemorrhage

Thalamus

person concept date

Gene-ChipsSeizure

SeizureAlzheimer’sDiabetes

Z5937XZ5937XZ5937XZ5937X

Z5956XZ5956XZ5956XZ5956X

Z5956XZ5956XZ5956XZ5956X

Z5937X

raw value

3/43/43/43/4

3/93/93/93/9

5/25/25/25/2

4/6

i2b2 national center for biomedical computing i2b2 clinical research chart and hive architecture...

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